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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-13 06:28:01
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| likes
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| library_name
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kanishka/smolm-mlm-bpe-unmask-seed_111 | kanishka | 2023-09-16T18:09:09Z | 110 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"dataset:AO-CHILDES",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-16T04:18:06Z | ---
base_model: models/smolm-mlm/config.json
tags:
- generated_from_trainer
datasets:
- AO-CHILDES
metrics:
- accuracy
widget:
- text: Do you like your <mask> ?
- text: Look here . What is that <mask> ?
- text: Where is <mask> ?
model-index:
- name: smolm-mlm-bpe-unmask-seed_111
results: []
pipeline_tag: fill-mask
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-mlm-bpe-unmask-seed_111
This model is a fine-tuned version of [models/smolm-mlm/config.json](https://huggingface.co/models/smolm-mlm/config.json) on 5M words of American-English child-directed input.
It achieves the following results on the evaluation set:
- Loss: 2.6956
- Accuracy: 0.4492
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 512
- seed: 111
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 24000
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.5129 | 1.0 | 11938 | 3.4627 | 0.3523 |
| 3.319 | 2.0 | 23876 | 3.3322 | 0.3641 |
| 3.1577 | 3.0 | 35814 | 3.1841 | 0.3810 |
| 3.0357 | 4.0 | 47752 | 3.0588 | 0.3982 |
| 2.9606 | 5.0 | 59690 | 2.9535 | 0.4109 |
| 2.87 | 6.0 | 71628 | 2.8745 | 0.4221 |
| 2.7817 | 7.0 | 83566 | 2.8351 | 0.4284 |
| 2.7388 | 8.0 | 95504 | 2.7536 | 0.4417 |
| 2.6618 | 9.0 | 107442 | 2.7308 | 0.4424 |
| 2.6258 | 10.0 | 119380 | 2.6880 | 0.4522 |
### Framework versions
- Transformers 4.32.1
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3 |
Shishir1807/M2_llama | Shishir1807 | 2023-09-16T18:08:22Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-09-16T18:06:54Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/M2_llama",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/M2_llama",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/M2_llama",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/M2_llama" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=4096, bias=False)
(up_proj): Linear(in_features=4096, out_features=11008, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
ys7yoo/sts_roberta_large_lr1e-05_wd1e-03_ep5_ckpt | ys7yoo | 2023-09-16T18:06:16Z | 95 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"base_model:klue/roberta-large",
"base_model:finetune:klue/roberta-large",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-16T17:44:29Z | ---
base_model: klue/roberta-large
tags:
- generated_from_trainer
datasets:
- klue
model-index:
- name: sts_roberta_large_lr1e-05_wd1e-03_ep5_ckpt
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sts_roberta_large_lr1e-05_wd1e-03_ep5_ckpt
This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3718
- Mse: 0.3718
- Mae: 0.4467
- R2: 0.8298
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 1.4636 | 1.0 | 183 | 0.6353 | 0.6353 | 0.5960 | 0.7091 |
| 0.1439 | 2.0 | 366 | 0.4268 | 0.4268 | 0.4874 | 0.8046 |
| 0.1016 | 3.0 | 549 | 0.4241 | 0.4241 | 0.4769 | 0.8058 |
| 0.0796 | 4.0 | 732 | 0.4218 | 0.4218 | 0.4811 | 0.8069 |
| 0.0651 | 5.0 | 915 | 0.3718 | 0.3718 | 0.4467 | 0.8298 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
|
CyberHarem/laura_stuart_toarumajutsunoindex | CyberHarem | 2023-09-16T18:02:37Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/laura_stuart_toarumajutsunoindex",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-16T17:50:22Z | ---
license: mit
datasets:
- CyberHarem/laura_stuart_toarumajutsunoindex
pipeline_tag: text-to-image
tags:
- art
---
# Lora of laura_stuart_toarumajutsunoindex
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5100, you need to download `5100/laura_stuart_toarumajutsunoindex.pt` as the embedding and `5100/laura_stuart_toarumajutsunoindex.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 5100**, with the score of 0.774. The trigger words are:
1. `laura_stuart_toarumajutsunoindex`
2. `blonde_hair, long_hair, blue_eyes`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **5100** | **0.774** | [**Download**](5100/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.726 | [Download](4760/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.739 | [Download](4420/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.694 | [Download](4080/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.626 | [Download](3740/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.680 | [Download](3400/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.744 | [Download](3060/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.741 | [Download](2720/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.562 | [Download](2380/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.455 | [Download](2040/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.588 | [Download](1700/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.487 | [Download](1360/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.522 | [Download](1020/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.275 | [Download](680/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.186 | [Download](340/laura_stuart_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
VictorGil75/workshops-setfit-model_V2 | VictorGil75 | 2023-09-16T17:44:01Z | 3 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
]
| text-classification | 2023-09-16T17:43:07Z | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# VictorGil75/workshops-setfit-model_V2
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("VictorGil75/workshops-setfit-model_V2")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
andreipb/roberta-poetry-religion-crpo | andreipb | 2023-09-16T17:31:51Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-13T15:18:08Z | ---
license: mit
language:
- en
pipeline_tag: fill-mask
library_name: transformers
widget:
- text: "This morning, the CEO was <mask>."
example_title: "Example 1"
- text: "Yesterday, all the students were <mask> in the park."
example_title: "Example 2"
- text: "All the children seemed <mask>."
example_title: "Example 3"
- text: "I opened the door and found a <mask> behind it."
example_title: "Example 4"
- text: "We went to see the <mask> movie."
example_title: "Example 5"
---
# roberta-poetry-religion-crpo
This model is based on the [RoBERTa base model](https://huggingface.co/roberta-base) (125M parameters)
fine-tuned for 20 epochs on a poetry dataset of 38 MB. This dataset was extracted from
the [Gutenberg Poetry Corpus](https://github.com/aparrish/gutenberg-poetry-corpus) using an automatic classifier
for poems in relation with the topic of **religion and spirituality**.
The model replaces a masked word, indicated by the `<mask>` tag, with a word associated with **religion and spirituality**, while preserving fluency.
Caution: the topic (here, **religion and spirituality**) only biases the choice of words with respect to the base model, but do not expect to find
only words strongly associated to this topic.
This model was trained by [Teo Ferrari](https://www.linkedin.com/in/teo-ferrari-0a4009176/)
as part of his Bachelor thesis at [HEIG-VD](https://gaps.heig-vd.ch/public/diplome/rapports.php?id=6763),
supervised by [Andrei Popescu-Belis](http://iict-space.heig-vd.ch/apu/).
The model is described in "[GPoeT: a Language Model Trained for Rhyme Generation on Synthetic Data](https://aclanthology.org/2023.latechclfl-1.2/)"
and is used in the [CR-PO](https://github.com/heig-iict-ida/crpo) system for [interactive poem generation](https://aclanthology.org/2022.lrec-1.377),
along with several other models for specific topics or emotions.
|
IlyaGusev/saiga2_7b_lora | IlyaGusev | 2023-09-16T16:53:55Z | 0 | 31 | null | [
"conversational",
"ru",
"dataset:IlyaGusev/ru_turbo_alpaca",
"dataset:IlyaGusev/ru_turbo_saiga",
"dataset:IlyaGusev/ru_sharegpt_cleaned",
"dataset:IlyaGusev/oasst1_ru_main_branch",
"dataset:IlyaGusev/ru_turbo_alpaca_evol_instruct",
"dataset:lksy/ru_instruct_gpt4",
"license:cc-by-4.0",
"region:us"
]
| text-generation | 2023-07-21T08:16:20Z | ---
datasets:
- IlyaGusev/ru_turbo_alpaca
- IlyaGusev/ru_turbo_saiga
- IlyaGusev/ru_sharegpt_cleaned
- IlyaGusev/oasst1_ru_main_branch
- IlyaGusev/ru_turbo_alpaca_evol_instruct
- lksy/ru_instruct_gpt4
language:
- ru
pipeline_tag: conversational
license: cc-by-4.0
---
# Saiga2 7B, Russian LLaMA2-based chatbot
Based on [LLaMA-2 7B HF](https://huggingface.co/meta-llama/Llama-2-7b-hf).
This is an adapter-only version.
Llama.cpp version: [link](https://huggingface.co/IlyaGusev/saiga2_7b_ggml).
Colab: [link](https://colab.research.google.com/drive/1Iw2TROpW0EjagyckyP-I-OWhLnLr7v2k).
Training code: [link](https://github.com/IlyaGusev/rulm/tree/master/self_instruct).
**WARNING 1**: Run with the development version of `transformers` and `peft`!
**WARNING 2**: Avoid using V100 (in Colab, for example). Outputs are much worse in this case.
**WARNING 3**: You can use the [Llama-2-7B-fp16](https://huggingface.co/TheBloke/Llama-2-7B-fp16) base model instead.
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
MODEL_NAME = "IlyaGusev/saiga2_7b_lora"
DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n"
DEFAULT_SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
class Conversation:
def __init__(
self,
message_template=DEFAULT_MESSAGE_TEMPLATE,
system_prompt=DEFAULT_SYSTEM_PROMPT,
start_token_id=1,
bot_token_id=9225
):
self.message_template = message_template
self.start_token_id = start_token_id
self.bot_token_id = bot_token_id
self.messages = [{
"role": "system",
"content": system_prompt
}]
def get_start_token_id(self):
return self.start_token_id
def get_bot_token_id(self):
return self.bot_token_id
def add_user_message(self, message):
self.messages.append({
"role": "user",
"content": message
})
def add_bot_message(self, message):
self.messages.append({
"role": "bot",
"content": message
})
def get_prompt(self, tokenizer):
final_text = ""
for message in self.messages:
message_text = self.message_template.format(**message)
final_text += message_text
final_text += tokenizer.decode([self.start_token_id, self.bot_token_id])
return final_text.strip()
def generate(model, tokenizer, prompt, generation_config):
data = tokenizer(prompt, return_tensors="pt")
data = {k: v.to(model.device) for k, v in data.items()}
output_ids = model.generate(
**data,
generation_config=generation_config
)[0]
output_ids = output_ids[len(data["input_ids"][0]):]
output = tokenizer.decode(output_ids, skip_special_tokens=True)
return output.strip()
config = PeftConfig.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
load_in_8bit=True,
torch_dtype=torch.float16,
device_map="auto"
)
model = PeftModel.from_pretrained(
model,
MODEL_NAME,
torch_dtype=torch.float16
)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
print(generation_config)
inputs = ["Почему трава зеленая?", "Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч"]
for inp in inputs:
conversation = Conversation()
conversation.add_user_message(inp)
prompt = conversation.get_prompt(tokenizer)
output = generate(model, tokenizer, prompt, generation_config)
print(inp)
print(output)
print()
print("==============================")
print()
```
Examples:
```
User: Почему трава зеленая?
Saiga: Зеленый цвет травы связан с наличием хлорофилла в её клетках. Хлорофилл - это пигмент, который обеспечивает защиту от ультрафиолетового излучения солнца. В процессе фотосинтеза растения используют энергию света для создания кислорода и органических веществ, таких как сахара. Ультрафиолетовое излучение может повредить хлорофиллы, поэтому они окрашиваются в зеленый цвет, чтобы предотвратить их повреждение.
```
```
User: Сочини длинный рассказ, обязательно упоминая следующие объекты. Дано: Таня, мяч
Saiga: Жила-была девочка по имени Таня. Она была очень активной и любила играть в футбол со своими друзьями. Однажды она купила новый мяч, который был еще более красивым и качественным, чем все остальные. Он был настолько же красочен, что казалось, будто он сам играет!
Однако, когда Танечка начала играть с ним, то заметила, что мяч не так уж и легкий, как она думала. Вместо того чтобы лететь высоко вверх, он падал на землю, и Таня чувствовала себя очень грустной. Но она решила, что это просто ее плохая игровая форма, и продолжила играть.
Но уже через несколько минут после того, как Таня бросала мяч в воздух, он завис над головой и начал медленно кружить вокруг. Таня стала беспокойна и испуганна - что случилось? Как ее мяч стал таким тяжелым?
В этот момент, Таня услышала шум из ближайшего парка. Она поспешно отправилась туда, чтобы узнать, что происходит. Когда она подошла к парку, она увидела маленького мальчика, который сидел на дереве и плевал на свой мяч.
"Что ты делаешь?" - спросила Таня. Мальчик повернулся к ней и показал ей свой мяч. "Этот мяч мне нравится больше всего", - говорил он. "Я хочу, чтобы он был такой же, как твой".
Таня поняла, что её мяч перестал быть для нее только игровым предметом, но и символом дружбы и общения. Она решила вернуться домой и взять его с собой.
Когда они пришли домой, Таня дала своему другу мяч и объяснила ему, что теперь их два мяча будут лучше вместе, чем один. Именно так, благодаря дружбе и сотрудничеству, Таня и ее новый друг стали лучшими командами. Их мячи стали играть вместе, и каждый из них становился еще лучше и быстрее благодаря друг другу.
Итак, Таня научилась, что настоящая дружба - это не только общительность и веселость, но и помощь и поддержка друг друга в трудных ситуациях. Благодаря своей новому другу, Таня научилась ценить своих товарищей и находить радость в том, что можно учиться у других людей.
```
v2:
- context extended to 4096
- wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/ktxsy0v2)
- saiga2_7b_v2 vs saiga2_7b: 85-23-68
v1:
- dataset code revision 7712a061d993f61c49b1e2d992e893c48acb3a87
- wandb [link](https://wandb.ai/ilyagusev/rulm_self_instruct/runs/innzu7g8)
- 7 datasets: ru_turbo_alpaca, ru_turbo_saiga, ru_sharegpt_cleaned, oasst1_ru_main_branch, gpt_roleplay_realm, ru_turbo_alpaca_evol_instruct (iteration 1/2), ru_instruct_gpt4
- Datasets merging script: [create_chat_set.py](https://github.com/IlyaGusev/rulm/blob/e4238fd9a196405b566a2d5838ab44b7a0f4dc31/self_instruct/src/data_processing/create_chat_set.py)
- saiga7b_v5 vs saiga2_7b: 78-8-90
|
ridwansukri/emotion_classification_v1 | ridwansukri | 2023-09-16T16:52:04Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-16T15:03:53Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_classification_v1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.575
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_classification_v1
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1905
- Accuracy: 0.575
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 10 | 2.0278 | 0.2437 |
| No log | 2.0 | 20 | 1.8875 | 0.3875 |
| No log | 3.0 | 30 | 1.6890 | 0.4313 |
| No log | 4.0 | 40 | 1.5484 | 0.5 |
| No log | 5.0 | 50 | 1.4799 | 0.5125 |
| No log | 6.0 | 60 | 1.4148 | 0.5375 |
| No log | 7.0 | 70 | 1.3529 | 0.5375 |
| No log | 8.0 | 80 | 1.3120 | 0.5312 |
| No log | 9.0 | 90 | 1.2790 | 0.5813 |
| No log | 10.0 | 100 | 1.2498 | 0.575 |
| No log | 11.0 | 110 | 1.2610 | 0.525 |
| No log | 12.0 | 120 | 1.1896 | 0.5938 |
| No log | 13.0 | 130 | 1.2251 | 0.5312 |
| No log | 14.0 | 140 | 1.2019 | 0.575 |
| No log | 15.0 | 150 | 1.1797 | 0.5563 |
| No log | 16.0 | 160 | 1.2484 | 0.5437 |
| No log | 17.0 | 170 | 1.1766 | 0.5875 |
| No log | 18.0 | 180 | 1.2401 | 0.4938 |
| No log | 19.0 | 190 | 1.1977 | 0.5312 |
| No log | 20.0 | 200 | 1.1839 | 0.5875 |
| No log | 21.0 | 210 | 1.2028 | 0.5687 |
| No log | 22.0 | 220 | 1.2048 | 0.5625 |
| No log | 23.0 | 230 | 1.2637 | 0.5375 |
| No log | 24.0 | 240 | 1.2371 | 0.5375 |
| No log | 25.0 | 250 | 1.2777 | 0.5687 |
| No log | 26.0 | 260 | 1.2544 | 0.525 |
| No log | 27.0 | 270 | 1.2104 | 0.5625 |
| No log | 28.0 | 280 | 1.1372 | 0.5938 |
| No log | 29.0 | 290 | 1.2405 | 0.575 |
| No log | 30.0 | 300 | 1.1624 | 0.6062 |
| No log | 31.0 | 310 | 1.2376 | 0.5875 |
| No log | 32.0 | 320 | 1.1794 | 0.5875 |
| No log | 33.0 | 330 | 1.2156 | 0.5563 |
| No log | 34.0 | 340 | 1.1725 | 0.55 |
| No log | 35.0 | 350 | 1.2394 | 0.55 |
| No log | 36.0 | 360 | 1.1886 | 0.5938 |
| No log | 37.0 | 370 | 1.1760 | 0.6188 |
| No log | 38.0 | 380 | 1.2757 | 0.525 |
| No log | 39.0 | 390 | 1.1703 | 0.6062 |
| No log | 40.0 | 400 | 1.2734 | 0.575 |
| No log | 41.0 | 410 | 1.2265 | 0.5563 |
| No log | 42.0 | 420 | 1.2651 | 0.5687 |
| No log | 43.0 | 430 | 1.2419 | 0.5813 |
| No log | 44.0 | 440 | 1.1871 | 0.6 |
| No log | 45.0 | 450 | 1.2542 | 0.575 |
| No log | 46.0 | 460 | 1.1910 | 0.5813 |
| No log | 47.0 | 470 | 1.1990 | 0.6 |
| No log | 48.0 | 480 | 1.2097 | 0.5813 |
| No log | 49.0 | 490 | 1.2226 | 0.5875 |
| 0.699 | 50.0 | 500 | 1.2793 | 0.5375 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
117jahfar/my-cats-new | 117jahfar | 2023-09-16T16:42:49Z | 1 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-09-16T16:29:48Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-cats-new Dreambooth model trained by 117jahfar following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: MESCOE223
Sample pictures of this concept:
|
alexeynoskov/rl_course_vizdoom_health_gathering_supreme | alexeynoskov | 2023-09-16T16:39:56Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-16T16:39:51Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 8.32 +/- 3.27
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r alexeynoskov/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Unstoppable0/modexla | Unstoppable0 | 2023-09-16T16:39:54Z | 0 | 0 | null | [
"arxiv:2211.06679",
"region:us"
]
| null | 2023-09-16T16:08:11Z | # Stable Diffusion web UI
A browser interface based on Gradio library for Stable Diffusion.
## Features
[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
- Original txt2img and img2img modes
- One click install and run script (but you still must install python and git)
- Outpainting
- Inpainting
- Color Sketch
- Prompt Matrix
- Stable Diffusion Upscale
- Attention, specify parts of text that the model should pay more attention to
- a man in a `((tuxedo))` - will pay more attention to tuxedo
- a man in a `(tuxedo:1.21)` - alternative syntax
- select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user)
- Loopback, run img2img processing multiple times
- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
- Textual Inversion
- have as many embeddings as you want and use any names you like for them
- use multiple embeddings with different numbers of vectors per token
- works with half precision floating point numbers
- train embeddings on 8GB (also reports of 6GB working)
- Extras tab with:
- GFPGAN, neural network that fixes faces
- CodeFormer, face restoration tool as an alternative to GFPGAN
- RealESRGAN, neural network upscaler
- ESRGAN, neural network upscaler with a lot of third party models
- SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
- LDSR, Latent diffusion super resolution upscaling
- Resizing aspect ratio options
- Sampling method selection
- Adjust sampler eta values (noise multiplier)
- More advanced noise setting options
- Interrupt processing at any time
- 4GB video card support (also reports of 2GB working)
- Correct seeds for batches
- Live prompt token length validation
- Generation parameters
- parameters you used to generate images are saved with that image
- in PNG chunks for PNG, in EXIF for JPEG
- can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
- can be disabled in settings
- drag and drop an image/text-parameters to promptbox
- Read Generation Parameters Button, loads parameters in promptbox to UI
- Settings page
- Running arbitrary python code from UI (must run with `--allow-code` to enable)
- Mouseover hints for most UI elements
- Possible to change defaults/mix/max/step values for UI elements via text config
- Tiling support, a checkbox to create images that can be tiled like textures
- Progress bar and live image generation preview
- Can use a separate neural network to produce previews with almost none VRAM or compute requirement
- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
- Styles, a way to save part of prompt and easily apply them via dropdown later
- Variations, a way to generate same image but with tiny differences
- Seed resizing, a way to generate same image but at slightly different resolution
- CLIP interrogator, a button that tries to guess prompt from an image
- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
- Batch Processing, process a group of files using img2img
- Img2img Alternative, reverse Euler method of cross attention control
- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
- Reloading checkpoints on the fly
- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
- separate prompts using uppercase `AND`
- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
- DeepDanbooru integration, creates danbooru style tags for anime prompts
- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
- Generate forever option
- Training tab
- hypernetworks and embeddings options
- Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
- Clip skip
- Hypernetworks
- Loras (same as Hypernetworks but more pretty)
- A separate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt
- Can select to load a different VAE from settings screen
- Estimated completion time in progress bar
- API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
- Now without any bad letters!
- Load checkpoints in safetensors format
- Eased resolution restriction: generated image's dimension must be a multiple of 8 rather than 64
- Now with a license!
- Reorder elements in the UI from settings screen
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for:
- [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended)
- [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
- [Intel CPUs, Intel GPUs (both integrated and discrete)](https://github.com/openvinotoolkit/stable-diffusion-webui/wiki/Installation-on-Intel-Silicon) (external wiki page)
Alternatively, use online services (like Google Colab):
- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Installation on Windows 10/11 with NVidia-GPUs using release package
1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
2. Run `update.bat`.
3. Run `run.bat`.
> For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
### Automatic Installation on Windows
1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
2. Install [git](https://git-scm.com/download/win).
3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
### Automatic Installation on Linux
1. Install the dependencies:
```bash
# Debian-based:
sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
# Red Hat-based:
sudo dnf install wget git python3
# Arch-based:
sudo pacman -S wget git python3
```
2. Navigate to the directory you would like the webui to be installed and execute the following command:
```bash
wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh
```
3. Run `webui.sh`.
4. Check `webui-user.sh` for options.
### Installation on Apple Silicon
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
## Contributing
Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
## Documentation
The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki).
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- GFPGAN - https://github.com/TencentARC/GFPGAN.git
- CodeFormer - https://github.com/sczhou/CodeFormer
- ESRGAN - https://github.com/xinntao/ESRGAN
- SwinIR - https://github.com/JingyunLiang/SwinIR
- Swin2SR - https://github.com/mv-lab/swin2sr
- LDSR - https://github.com/Hafiidz/latent-diffusion
- MiDaS - https://github.com/isl-org/MiDaS
- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- xformers - https://github.com/facebookresearch/xformers
- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
- Security advice - RyotaK
- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
- LyCORIS - KohakuBlueleaf
- Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
- (You)
|
cgijoe/ppo-LunarLander-v2 | cgijoe | 2023-09-16T16:34:23Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-05T20:39:43Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -658.36 +/- 50.44
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CyberHarem/vento_of_the_front_toarumajutsunoindex | CyberHarem | 2023-09-16T16:33:53Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/vento_of_the_front_toarumajutsunoindex",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-16T16:22:55Z | ---
license: mit
datasets:
- CyberHarem/vento_of_the_front_toarumajutsunoindex
pipeline_tag: text-to-image
tags:
- art
---
# Lora of vento_of_the_front_toarumajutsunoindex
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 4420, you need to download `4420/vento_of_the_front_toarumajutsunoindex.pt` as the embedding and `4420/vento_of_the_front_toarumajutsunoindex.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4420**, with the score of 0.967. The trigger words are:
1. `vento_of_the_front_toarumajutsunoindex`
2. `blonde_hair, piercing, blue_eyes`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.844 | [Download](5100/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.960 | [Download](4760/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| **4420** | **0.967** | [**Download**](4420/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.916 | [Download](4080/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.902 | [Download](3740/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.889 | [Download](3400/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.767 | [Download](3060/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.830 | [Download](2720/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.846 | [Download](2380/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.760 | [Download](2040/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.782 | [Download](1700/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.684 | [Download](1360/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.610 | [Download](1020/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.469 | [Download](680/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.441 | [Download](340/vento_of_the_front_toarumajutsunoindex.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
winglian/omega-3b | winglian | 2023-09-16T16:32:13Z | 31 | 3 | transformers | [
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"custom_code",
"en",
"dataset:nampdn-ai/tiny-textbooks",
"dataset:nampdn-ai/tiny-lessons",
"autotrain_compatible",
"region:us"
]
| text-generation | 2023-09-15T07:40:11Z | ---
datasets:
- nampdn-ai/tiny-textbooks
- nampdn-ai/tiny-lessons
language:
- en
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# Omega 2.6B
This model is derived from phi 1.3B using layer stacking techniques to double the number of hidden layers in the model.
The model was then trained for 1 epoch on data from tiny-textbooks and tiny-lessons.
# Training
https://wandb.ai/wing-lian/phi-2x-pt-tiny
|
ygaci/Taxi-v3 | ygaci | 2023-09-16T16:07:51Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-16T15:40:27Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ygaci/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
elenaThevalley/mobilenet_v2_1.0_224-finetuned-32bs-0.01lr | elenaThevalley | 2023-09-16T16:05:57Z | 194 | 0 | transformers | [
"transformers",
"pytorch",
"mobilenet_v2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/mobilenet_v2_1.0_224",
"base_model:finetune:google/mobilenet_v2_1.0_224",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-15T15:48:51Z | ---
license: other
base_model: google/mobilenet_v2_1.0_224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: mobilenet_v2_1.0_224-finetuned-32bs-0.01lr
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9206560874783305
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mobilenet_v2_1.0_224-finetuned-32bs-0.01lr
This model is a fine-tuned version of [google/mobilenet_v2_1.0_224](https://huggingface.co/google/mobilenet_v2_1.0_224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2230
- Accuracy: 0.9207
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.01
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.99 | 53 | 0.3603 | 0.8745 |
| No log | 1.99 | 107 | 0.2323 | 0.9183 |
| No log | 2.96 | 159 | 0.2230 | 0.9207 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
SuperSecureHuman/t5_base_trails | SuperSecureHuman | 2023-09-16T15:52:29Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_books",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-09-16T14:54:35Z | ---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
datasets:
- opus_books
model-index:
- name: t5_base_trails
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5_base_trails
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the opus_books dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
InexperiencedMe/a2c-PandaReachDense-v3 | InexperiencedMe | 2023-09-16T15:48:07Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-16T15:42:41Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.21 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ctrltokyo/phi-1_5-finetuned-gsm8k | ctrltokyo | 2023-09-16T15:46:17Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:microsoft/phi-1_5",
"base_model:finetune:microsoft/phi-1_5",
"license:other",
"region:us"
]
| null | 2023-09-16T15:42:32Z | ---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-gsm8k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# phi-1_5-finetuned-gsm8k
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
huygdng/wav2vec2-large-xls-r-300m-tw-trans-colab-2 | huygdng | 2023-09-16T15:41:58Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-09-16T14:28:08Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-tw-trans-colab-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-tw-trans-colab-2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 100
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
byoussef/whisper-large-v2-Ko | byoussef | 2023-09-16T15:31:52Z | 233 | 20 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ko",
"dataset:Bingsu/zeroth-korean",
"base_model:openai/whisper-large-v2",
"base_model:finetune:openai/whisper-large-v2",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-03-10T07:11:45Z | ---
language:
- ko
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- Bingsu/zeroth-korean
metrics:
- wer
pipeline_tag: automatic-speech-recognition
base_model: openai/whisper-large-v2
model-index:
- name: whisper-large-v2-Ko
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Bingsu/zeroth-korean
type: Bingsu/zeroth-korean
metrics:
- type: wer
value: 2.9
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: ko_kr
split: test
metrics:
- type: wer
value: 20.66
name: WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-v2-Ko
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0617
- Wer: **2.9**
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
***** train metrics *****
epoch = 50.0
train_loss = 0.0234
train_runtime = 16:20:18.00
train_samples = 22262
train_samples_per_second = 19.042
train_steps_per_second = 0.085
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- total_train_batch_size: 224
- total_eval_batch_size: 112
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0299 | 10.0 | 1000 | 0.0745 | 0.0447 |
| 0.0085 | 20.0 | 2000 | 0.0608 | 0.0353 |
| 0.0036 | 30.0 | 3000 | 0.0593 | 0.0302 |
| 0.0013 | 40.0 | 4000 | 0.0609 | 0.0282 |
| 0.0008 | 50.0 | 5000 | 0.0617 | 0.0290 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2 |
salim4n/dqn-SpaceInvadersNoFrameskip-v4 | salim4n | 2023-09-16T15:29:24Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-16T15:28:53Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 385.00 +/- 136.99
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga salim4n -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga salim4n -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga salim4n
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Norah-K/PDPL_CAMeLBERT | Norah-K | 2023-09-16T15:28:59Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:NorahNasser/autotrain-data-camel_pdpl",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-16T15:25:01Z | ---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain"
datasets:
- NorahNasser/autotrain-data-camel_pdpl
co2_eq_emissions:
emissions: 0.04275306009908754
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 89564143964
- CO2 Emissions (in grams): 0.0428
## Validation Metrics
- Loss: 0.293
- Accuracy: 0.929
- Macro F1: 0.926
- Micro F1: 0.929
- Weighted F1: 0.929
- Macro Precision: 0.936
- Micro Precision: 0.929
- Weighted Precision: 0.930
- Macro Recall: 0.918
- Micro Recall: 0.929
- Weighted Recall: 0.929
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/NorahNasser/autotrain-camel_pdpl-89564143964
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("NorahNasser/autotrain-camel_pdpl-89564143964", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("NorahNasser/autotrain-camel_pdpl-89564143964", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
``` |
sd-concepts-library/finn-token | sd-concepts-library | 2023-09-16T15:16:11Z | 0 | 0 | null | [
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:mit",
"region:us"
]
| null | 2023-09-16T15:16:10Z | ---
license: mit
base_model: runwayml/stable-diffusion-v1-5
---
### finn-token on Stable Diffusion
This is the `<finn-token>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
anyuanay/my_awesome_model | anyuanay | 2023-09-16T15:14:44Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-16T14:57:41Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
- Accuracy: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 157 | 0.0004 | 1.0 |
| No log | 2.0 | 314 | 0.0002 | 1.0 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
DaunIDebil/Zelensky-RVC2.0 | DaunIDebil | 2023-09-16T15:11:22Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-09-16T15:07:23Z | model is not mine. i found it somewhere on the internet in may. |
kanishka/smolm-mlm-bpe-unmask-seed_222 | kanishka | 2023-09-16T15:09:48Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-16T06:21:18Z | ---
base_model: models/smolm-mlm/config.json
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smolm-mlm-bpe-unmask-seed_222
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-mlm-bpe-unmask-seed_222
This model is a fine-tuned version of [models/smolm-mlm/config.json](https://huggingface.co/models/smolm-mlm/config.json) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7005
- Accuracy: 0.4481
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 512
- seed: 222
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 24000
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.5334 | 1.0 | 11938 | 3.4896 | 0.3463 |
| 3.3402 | 2.0 | 23876 | 3.3814 | 0.3590 |
| 3.1641 | 3.0 | 35814 | 3.1702 | 0.3844 |
| 3.0325 | 4.0 | 47752 | 3.0475 | 0.4019 |
| 2.951 | 5.0 | 59690 | 2.9666 | 0.4095 |
| 2.8583 | 6.0 | 71628 | 2.8908 | 0.4201 |
| 2.7872 | 7.0 | 83566 | 2.8299 | 0.4310 |
| 2.7348 | 8.0 | 95504 | 2.7900 | 0.4335 |
| 2.6584 | 9.0 | 107442 | 2.7272 | 0.4443 |
| 2.6462 | 10.0 | 119380 | 2.6962 | 0.4501 |
### Framework versions
- Transformers 4.32.1
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
kanishka/smolm-mlm-bpe-unmask-seed_444 | kanishka | 2023-09-16T15:09:41Z | 109 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-16T10:25:20Z | ---
base_model: models/smolm-mlm/config.json
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smolm-mlm-bpe-unmask-seed_444
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smolm-mlm-bpe-unmask-seed_444
This model is a fine-tuned version of [models/smolm-mlm/config.json](https://huggingface.co/models/smolm-mlm/config.json) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7042
- Accuracy: 0.4475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 512
- seed: 444
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 24000
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.5523 | 1.0 | 11938 | 3.4904 | 0.3512 |
| 3.3151 | 2.0 | 23876 | 3.3192 | 0.3678 |
| 3.1526 | 3.0 | 35814 | 3.1545 | 0.3894 |
| 3.0031 | 4.0 | 47752 | 3.0706 | 0.3971 |
| 2.9668 | 5.0 | 59690 | 2.9606 | 0.4121 |
| 2.8548 | 6.0 | 71628 | 2.8717 | 0.4244 |
| 2.7686 | 7.0 | 83566 | 2.8147 | 0.4311 |
| 2.7195 | 8.0 | 95504 | 2.7657 | 0.4389 |
| 2.6659 | 9.0 | 107442 | 2.7103 | 0.4483 |
| 2.6254 | 10.0 | 119380 | 2.6990 | 0.4510 |
### Framework versions
- Transformers 4.32.1
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AliBagherz/result | AliBagherz | 2023-09-16T15:03:20Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:pquad",
"base_model:HooshvareLab/bert-fa-base-uncased",
"base_model:finetune:HooshvareLab/bert-fa-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-09-16T14:56:38Z | ---
license: apache-2.0
base_model: HooshvareLab/bert-fa-base-uncased
tags:
- generated_from_trainer
datasets:
- pquad
model-index:
- name: result
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# result
This model is a fine-tuned version of [HooshvareLab/bert-fa-base-uncased](https://huggingface.co/HooshvareLab/bert-fa-base-uncased) on the pquad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 4.3084 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
QMB15/mythomax-L2-13B-4.625bit-exl2 | QMB15 | 2023-09-16T14:51:14Z | 5 | 4 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-09-16T14:35:16Z | ---
license: other
language:
- en
---
This is an exllama V2 quantization of https://huggingface.co/Gryphe/MythoMax-L2-13b
Uses a target bpw of 4.625.
Includes measurement.json for convenience of quantizing to other sizes.
Calibration data: https://huggingface.co/datasets/wikitext/resolve/refs%2Fconvert%2Fparquet/wikitext-2-v1/test/0000.parquet
An improved, potentially even perfected variant of MythoMix, my [MythoLogic-L2](https://huggingface.co/Gryphe/MythoLogic-L2-13b) and [Huginn](https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-FP16) merge using a highly experimental tensor type merge technique. The main difference with MythoMix is that I allowed more of Huginn to intermingle with the single tensors located at the front and end of a model, resulting in increased coherency across the entire structure.
The script and the acccompanying templates I used to produce both can [be found here](https://github.com/Gryphe/BlockMerge_Gradient/tree/main/YAML).
This model is proficient at both roleplaying and storywriting due to its unique nature.
Quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoMax-L2-13B-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoMax-L2-13B-GPTQ) (You're the best!)
## Model details
The idea behind this merge is that each layer is composed of several tensors, which are in turn responsible for specific functions. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to have resulted in a model that exceeds at both, confirming my theory. (More details to be released at a later time)
This type of merge is incapable of being illustrated, as each of its 363 tensors had an unique ratio applied to it. As with my prior merges, gradients were part of these ratios to further finetune its behaviour.
## Prompt Format
This model primarily uses Alpaca formatting, so for optimal model performance, use:
```
<System prompt/Character Card>
### Instruction:
Your instruction or question here.
For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.
### Response:
```
---
license: other
--- |
ygaci/ppo-Huggy | ygaci | 2023-09-16T14:26:15Z | 11 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-09-16T14:26:05Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ygaci/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CyberHarem/tsukuyomi_komoe_toarumajutsunoindex | CyberHarem | 2023-09-16T14:20:13Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/tsukuyomi_komoe_toarumajutsunoindex",
"license:mit",
"region:us"
]
| text-to-image | 2023-08-16T07:41:46Z | ---
license: mit
datasets:
- CyberHarem/tsukuyomi_komoe_toarumajutsunoindex
pipeline_tag: text-to-image
tags:
- art
---
# Lora of tsukuyomi_komoe_toarumajutsunoindex
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6380, you need to download `6380/tsukuyomi_komoe_toarumajutsunoindex.pt` as the embedding and `6380/tsukuyomi_komoe_toarumajutsunoindex.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6380**, with the score of 0.879. The trigger words are:
1. `tsukuyomi_komoe_toarumajutsunoindex`
2. `short_hair, pink_hair, pink_eyes, open_mouth`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | pattern_18 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 8700 | 0.831 | [Download](8700/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8700/previews/nude.png) | [<NSFW, click to see>](8700/previews/nude2.png) |  |  |
| 8120 | 0.855 | [Download](8120/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8120/previews/nude.png) | [<NSFW, click to see>](8120/previews/nude2.png) |  |  |
| 7540 | 0.873 | [Download](7540/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7540/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7540/previews/nude.png) | [<NSFW, click to see>](7540/previews/nude2.png) |  |  |
| 6960 | 0.868 | [Download](6960/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6960/previews/nude.png) | [<NSFW, click to see>](6960/previews/nude2.png) |  |  |
| **6380** | **0.879** | [**Download**](6380/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6380/previews/nude.png) | [<NSFW, click to see>](6380/previews/nude2.png) |  |  |
| 5800 | 0.838 | [Download](5800/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5800/previews/nude.png) | [<NSFW, click to see>](5800/previews/nude2.png) |  |  |
| 5220 | 0.720 | [Download](5220/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5220/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5220/previews/nude.png) | [<NSFW, click to see>](5220/previews/nude2.png) |  |  |
| 4640 | 0.811 | [Download](4640/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4640/previews/nude.png) | [<NSFW, click to see>](4640/previews/nude2.png) |  |  |
| 4060 | 0.696 | [Download](4060/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4060/previews/nude.png) | [<NSFW, click to see>](4060/previews/nude2.png) |  |  |
| 3480 | 0.703 | [Download](3480/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3480/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3480/previews/nude.png) | [<NSFW, click to see>](3480/previews/nude2.png) |  |  |
| 2900 | 0.612 | [Download](2900/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2900/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2900/previews/nude.png) | [<NSFW, click to see>](2900/previews/nude2.png) |  |  |
| 2320 | 0.400 | [Download](2320/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2320/previews/nude.png) | [<NSFW, click to see>](2320/previews/nude2.png) |  |  |
| 1740 | 0.439 | [Download](1740/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1740/previews/nude.png) | [<NSFW, click to see>](1740/previews/nude2.png) |  |  |
| 1160 | 0.190 | [Download](1160/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1160/previews/nude.png) | [<NSFW, click to see>](1160/previews/nude2.png) |  |  |
| 580 | 0.091 | [Download](580/tsukuyomi_komoe_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](580/previews/bondage.png) |  |  |  | [<NSFW, click to see>](580/previews/nude.png) | [<NSFW, click to see>](580/previews/nude2.png) |  |  |
|
LarryAIDraw/Horikita | LarryAIDraw | 2023-09-16T14:05:54Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-09-16T13:53:44Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/143817/suzune-horikita-or-you-zitsu |
LarryAIDraw/Iinomiko-v2-000010 | LarryAIDraw | 2023-09-16T14:00:17Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-09-16T13:57:08Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/146080/iino-miko-kaguya-sama-wa-kokurasetailove-is-war |
venkataravuri/my_awesome_swag_model | venkataravuri | 2023-09-16T13:48:26Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"multiple-choice",
"generated_from_trainer",
"dataset:swag",
"endpoints_compatible",
"region:us"
]
| multiple-choice | 2023-06-16T04:18:49Z | ---
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: my_awesome_swag_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_swag_model
This model was trained from scratch on the swag dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1711
- Accuracy: 0.746
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8738 | 1.0 | 625 | 0.6888 | 0.728 |
| 0.431 | 2.0 | 1250 | 0.7642 | 0.736 |
| 0.1891 | 3.0 | 1875 | 1.1711 | 0.746 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
espiusedwards/flant5-large-lora | espiusedwards | 2023-09-16T13:46:52Z | 23 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-01T11:45:54Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
loduis/test-distilroberta-base-mrpc-glue | loduis | 2023-09-16T13:46:40Z | 114 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-16T12:16:45Z | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: test-distilroberta-base-mrpc-glue
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: mrpc
split: validation
args: mrpc
metrics:
- name: Accuracy
type: accuracy
value: 0.8259803921568627
- name: F1
type: f1
value: 0.8794567062818336
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-distilroberta-base-mrpc-glue
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6306
- Accuracy: 0.8260
- F1: 0.8795
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5489 | 1.09 | 500 | 0.5670 | 0.8064 | 0.8699 |
| 0.393 | 2.18 | 1000 | 0.6306 | 0.8260 | 0.8795 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
fetiska/pyramid_raider | fetiska | 2023-09-16T13:44:24Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-09-16T13:44:16Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: fetiska/pyramid_raider
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CyberHarem/ninomiya_asuka_idolmastercinderellagirls | CyberHarem | 2023-09-16T13:38:49Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/ninomiya_asuka_idolmastercinderellagirls",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-16T13:23:12Z | ---
license: mit
datasets:
- CyberHarem/ninomiya_asuka_idolmastercinderellagirls
pipeline_tag: text-to-image
tags:
- art
---
# Lora of ninomiya_asuka_idolmastercinderellagirls
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 4800, you need to download `4800/ninomiya_asuka_idolmastercinderellagirls.pt` as the embedding and `4800/ninomiya_asuka_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4800**, with the score of 0.979. The trigger words are:
1. `ninomiya_asuka_idolmastercinderellagirls`
2. `multicolored_hair, two-tone_hair, long_hair, purple_eyes, orange_hair, hair_between_eyes, smile, bangs, brown_hair`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7200 | 0.961 | [Download](7200/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7200/previews/nude.png) | [<NSFW, click to see>](7200/previews/nude2.png) |  |  |
| 6720 | 0.952 | [Download](6720/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6720/previews/nude.png) | [<NSFW, click to see>](6720/previews/nude2.png) |  |  |
| 6240 | 0.971 | [Download](6240/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5760 | 0.963 | [Download](5760/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5760/previews/nude.png) | [<NSFW, click to see>](5760/previews/nude2.png) |  |  |
| 5280 | 0.956 | [Download](5280/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) |  |  |
| **4800** | **0.979** | [**Download**](4800/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) |  |  |
| 4320 | 0.979 | [Download](4320/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) |  |  |
| 3840 | 0.952 | [Download](3840/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3840/previews/nude.png) | [<NSFW, click to see>](3840/previews/nude2.png) |  |  |
| 3360 | 0.969 | [Download](3360/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3360/previews/nude.png) | [<NSFW, click to see>](3360/previews/nude2.png) |  |  |
| 2880 | 0.926 | [Download](2880/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2880/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2880/previews/nude.png) | [<NSFW, click to see>](2880/previews/nude2.png) |  |  |
| 2400 | 0.941 | [Download](2400/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) |  |  |
| 1920 | 0.980 | [Download](1920/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1920/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) |  |  |
| 1440 | 0.911 | [Download](1440/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1440/previews/nude.png) | [<NSFW, click to see>](1440/previews/nude2.png) |  |  |
| 960 | 0.890 | [Download](960/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](960/previews/nude.png) | [<NSFW, click to see>](960/previews/nude2.png) |  |  |
| 480 | 0.980 | [Download](480/ninomiya_asuka_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](480/previews/bondage.png) |  |  |  | [<NSFW, click to see>](480/previews/nude.png) | [<NSFW, click to see>](480/previews/nude2.png) |  |  |
|
LarryAIDraw/haruna_ba | LarryAIDraw | 2023-09-16T13:30:38Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-09-16T13:30:10Z | ---
license: creativeml-openrail-m
---
|
CyberHarem/takitsubo_rikou_toarumajutsunoindex | CyberHarem | 2023-09-16T13:08:35Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/takitsubo_rikou_toarumajutsunoindex",
"license:mit",
"region:us"
]
| text-to-image | 2023-08-15T23:44:24Z | ---
license: mit
datasets:
- CyberHarem/takitsubo_rikou_toarumajutsunoindex
pipeline_tag: text-to-image
tags:
- art
---
# Lora of takitsubo_rikou_toarumajutsunoindex
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 4080, you need to download `4080/takitsubo_rikou_toarumajutsunoindex.pt` as the embedding and `4080/takitsubo_rikou_toarumajutsunoindex.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4080**, with the score of 0.948. The trigger words are:
1. `takitsubo_rikou_toarumajutsunoindex`
2. `short_hair, black_hair, brown_eyes, brown_hair`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.921 | [Download](5100/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.939 | [Download](4760/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.937 | [Download](4420/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| **4080** | **0.948** | [**Download**](4080/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.945 | [Download](3740/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.799 | [Download](3400/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.924 | [Download](3060/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.941 | [Download](2720/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.914 | [Download](2380/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.939 | [Download](2040/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.856 | [Download](1700/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.838 | [Download](1360/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.810 | [Download](1020/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.757 | [Download](680/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.501 | [Download](340/takitsubo_rikou_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
jeffsabarman/image_classification | jeffsabarman | 2023-09-16T13:01:32Z | 217 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-16T13:00:54Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.60625
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1918
- Accuracy: 0.6062
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 20 | 1.6651 | 0.3187 |
| No log | 2.0 | 40 | 1.3900 | 0.475 |
| No log | 3.0 | 60 | 1.2950 | 0.4875 |
| No log | 4.0 | 80 | 1.2170 | 0.5813 |
| No log | 5.0 | 100 | 1.1709 | 0.5687 |
| No log | 6.0 | 120 | 1.2711 | 0.525 |
| No log | 7.0 | 140 | 1.1324 | 0.575 |
| No log | 8.0 | 160 | 1.2349 | 0.5437 |
| No log | 9.0 | 180 | 1.3844 | 0.5312 |
| No log | 10.0 | 200 | 1.2460 | 0.55 |
| No log | 11.0 | 220 | 1.2182 | 0.6125 |
| No log | 12.0 | 240 | 1.3365 | 0.5563 |
| No log | 13.0 | 260 | 1.2137 | 0.6125 |
| No log | 14.0 | 280 | 1.3335 | 0.575 |
| No log | 15.0 | 300 | 1.1078 | 0.625 |
| No log | 16.0 | 320 | 1.2962 | 0.6 |
| No log | 17.0 | 340 | 1.2558 | 0.6125 |
| No log | 18.0 | 360 | 1.3949 | 0.55 |
| No log | 19.0 | 380 | 1.3807 | 0.5687 |
| No log | 20.0 | 400 | 1.2734 | 0.6 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
bongo2112/sdxl-db-mbosso-headshot | bongo2112 | 2023-09-16T12:58:56Z | 3 | 3 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
]
| text-to-image | 2023-09-16T12:56:52Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of mshededembosso man
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
upro/blip | upro | 2023-09-16T12:37:39Z | 101 | 2 | transformers | [
"transformers",
"pytorch",
"tf",
"blip",
"image-text-to-text",
"image-captioning",
"image-to-text",
"arxiv:2201.12086",
"license:bsd-3-clause",
"endpoints_compatible",
"region:us"
]
| image-to-text | 2023-09-16T12:37:39Z | ---
pipeline_tag: image-to-text
tags:
- image-captioning
languages:
- en
license: bsd-3-clause
---
# BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Model card for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone).
|  |
|:--:|
| <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>|
## TL;DR
Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract:
*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*
## Usage
You can use this model for conditional and un-conditional image captioning
### Using the Pytorch model
#### Running the model on CPU
<details>
<summary> Click to expand </summary>
```python
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
#### Running the model on GPU
##### In full precision
<details>
<summary> Click to expand </summary>
```python
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to("cuda")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt").to("cuda")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to("cuda")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
```
</details>
##### In half precision (`float16`)
<details>
<summary> Click to expand </summary>
```python
import torch
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
# >>> a photography of a woman and her dog
# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
>>> a woman sitting on the beach with her dog
```
</details>
## BibTex and citation info
```
@misc{https://doi.org/10.48550/arxiv.2201.12086,
doi = {10.48550/ARXIV.2201.12086},
url = {https://arxiv.org/abs/2201.12086},
author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
``` |
huba-buba/speecht5_tts_voxpopuli_nl | huba-buba | 2023-09-16T12:36:11Z | 79 | 0 | transformers | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"test model",
"generated_from_trainer",
"nl",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2023-09-16T09:06:39Z | ---
language:
- nl
license: mit
base_model: microsoft/speecht5_tts
tags:
- test model
- generated_from_trainer
datasets:
- facebook/voxpopuli
model-index:
- name: SpeechT5 TTS Dutch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5 TTS Dutch
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4546
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5151 | 8.58 | 1000 | 0.4713 |
| 0.4916 | 17.17 | 2000 | 0.4599 |
| 0.4863 | 25.75 | 3000 | 0.4551 |
| 0.4896 | 34.33 | 4000 | 0.4546 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
CyberHarem/totoki_airi_idolmastercinderellagirls | CyberHarem | 2023-09-16T12:33:26Z | 0 | 1 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/totoki_airi_idolmastercinderellagirls",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-16T12:17:54Z | ---
license: mit
datasets:
- CyberHarem/totoki_airi_idolmastercinderellagirls
pipeline_tag: text-to-image
tags:
- art
---
# Lora of totoki_airi_idolmastercinderellagirls
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6480, you need to download `6480/totoki_airi_idolmastercinderellagirls.pt` as the embedding and `6480/totoki_airi_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6480**, with the score of 0.864. The trigger words are:
1. `totoki_airi_idolmastercinderellagirls`
2. `brown_hair, brown_eyes, twintails, blush, breasts, large_breasts, smile, open_mouth, bangs, cleavage, jewelry, hair_ornament`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:---------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 8100 | 0.805 | [Download](8100/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](8100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8100/previews/nude.png) | [<NSFW, click to see>](8100/previews/nude2.png) |  |  |
| 7560 | 0.815 | [Download](7560/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](7560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7560/previews/nude.png) | [<NSFW, click to see>](7560/previews/nude2.png) |  |  |
| 7020 | 0.812 | [Download](7020/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](7020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7020/previews/nude.png) | [<NSFW, click to see>](7020/previews/nude2.png) |  |  |
| **6480** | **0.864** | [**Download**](6480/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](6480/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6480/previews/nude.png) | [<NSFW, click to see>](6480/previews/nude2.png) |  |  |
| 5940 | 0.785 | [Download](5940/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](5940/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5940/previews/nude.png) | [<NSFW, click to see>](5940/previews/nude2.png) |  |  |
| 5400 | 0.822 | [Download](5400/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](5400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5400/previews/nude.png) | [<NSFW, click to see>](5400/previews/nude2.png) |  |  |
| 4860 | 0.840 | [Download](4860/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4860/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4860/previews/nude.png) | [<NSFW, click to see>](4860/previews/nude2.png) |  |  |
| 4320 | 0.813 | [Download](4320/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](4320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) |  |  |
| 3780 | 0.749 | [Download](3780/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3780/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3780/previews/nude.png) | [<NSFW, click to see>](3780/previews/nude2.png) |  |  |
| 3240 | 0.657 | [Download](3240/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](3240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3240/previews/nude.png) | [<NSFW, click to see>](3240/previews/nude2.png) |  |  |
| 2700 | 0.662 | [Download](2700/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2700/previews/nude.png) | [<NSFW, click to see>](2700/previews/nude2.png) |  |  |
| 2160 | 0.689 | [Download](2160/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](2160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2160/previews/nude.png) | [<NSFW, click to see>](2160/previews/nude2.png) |  |  |
| 1620 | 0.643 | [Download](1620/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1620/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1620/previews/nude.png) | [<NSFW, click to see>](1620/previews/nude2.png) |  |  |
| 1080 | 0.650 | [Download](1080/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](1080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1080/previews/nude.png) | [<NSFW, click to see>](1080/previews/nude2.png) |  |  |
| 540 | 0.246 | [Download](540/totoki_airi_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  | [<NSFW, click to see>](540/previews/bondage.png) |  |  |  | [<NSFW, click to see>](540/previews/nude.png) | [<NSFW, click to see>](540/previews/nude2.png) |  |  |
|
AlienKevin/whisper-small-jyutping-without-tones-full | AlienKevin | 2023-09-16T12:29:11Z | 76 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"yue",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-09-16T12:27:24Z | ---
language:
- yue
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Jyutping without Tones Full Version
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Jyutping without Tones Full Version
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 14.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0473
- Wer: 4.5089
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- training_steps: 2400
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0895 | 0.18 | 800 | 0.0864 | 8.1065 |
| 0.0622 | 0.35 | 1600 | 0.0576 | 5.4563 |
| 0.0555 | 0.53 | 2400 | 0.0473 | 4.5089 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
DomArruda/LunarLanderV2 | DomArruda | 2023-09-16T12:08:18Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-09-16T12:06:48Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 262.13 +/- 20.31
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
yumjunstar/trocr-small-printedkorean-deleteunusedchar_denoise | yumjunstar | 2023-09-16T11:52:36Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2023-08-30T17:15:10Z | ---
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: trocr-small-printedkorean-deleteunusedchar_denoise
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trocr-small-printedkorean-deleteunusedchar_denoise
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8528
- Cer: 0.3241
- Wer: 0.3436
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|
| 0.9789 | 0.11 | 1000 | 1.5260 | 0.2830 | 0.2979 |
| 1.0254 | 0.21 | 2000 | 1.5167 | 0.2827 | 0.2991 |
| 1.0925 | 0.32 | 3000 | 1.5093 | 0.2879 | 0.3036 |
| 1.0925 | 0.43 | 4000 | 1.4969 | 0.2872 | 0.3037 |
| 1.0883 | 0.53 | 5000 | 1.4893 | 0.2848 | 0.2998 |
| 1.0411 | 0.64 | 6000 | 1.4839 | 0.2832 | 0.2994 |
| 1.1372 | 0.75 | 7000 | 1.4771 | 0.2841 | 0.2993 |
| 1.1086 | 0.85 | 8000 | 1.4772 | 0.2838 | 0.3010 |
| 1.1658 | 0.96 | 9000 | 1.4725 | 0.2843 | 0.3013 |
| 0.9649 | 1.07 | 10000 | 1.5067 | 0.2825 | 0.2978 |
| 0.891 | 1.17 | 11000 | 1.5058 | 0.2846 | 0.2993 |
| 1.0862 | 1.28 | 12000 | 1.4938 | 0.2873 | 0.3001 |
| 1.0845 | 1.39 | 13000 | 1.4923 | 0.2867 | 0.3030 |
| 1.0306 | 1.49 | 14000 | 1.4905 | 0.2833 | 0.2976 |
| 0.9651 | 1.6 | 15000 | 1.4948 | 0.2835 | 0.2986 |
| 1.1189 | 1.71 | 16000 | 1.4811 | 0.2856 | 0.3018 |
| 1.0727 | 1.82 | 17000 | 1.4763 | 0.2864 | 0.3010 |
| 1.0587 | 1.92 | 18000 | 1.4782 | 0.2830 | 0.2994 |
| 0.9599 | 2.03 | 19000 | 1.5071 | 0.2866 | 0.3011 |
| 1.0625 | 2.14 | 20000 | 1.5029 | 0.2837 | 0.2991 |
| 1.1031 | 2.24 | 21000 | 1.5084 | 0.2880 | 0.3037 |
| 0.9586 | 2.35 | 22000 | 1.5068 | 0.2842 | 0.2967 |
| 1.0012 | 2.46 | 23000 | 1.4990 | 0.2861 | 0.3017 |
| 1.0871 | 2.56 | 24000 | 1.4905 | 0.2848 | 0.2989 |
| 0.9501 | 2.67 | 25000 | 1.4848 | 0.2835 | 0.2985 |
| 0.9761 | 2.78 | 26000 | 1.4860 | 0.2819 | 0.2983 |
| 1.0241 | 2.88 | 27000 | 1.4728 | 0.2813 | 0.2949 |
| 1.065 | 2.99 | 28000 | 1.4826 | 0.2829 | 0.2976 |
| 1.0412 | 3.1 | 29000 | 1.5160 | 0.2879 | 0.3022 |
| 0.9972 | 3.2 | 30000 | 1.5105 | 0.2867 | 0.3009 |
| 0.9758 | 3.31 | 31000 | 1.5037 | 0.2827 | 0.2962 |
| 0.9521 | 3.42 | 32000 | 1.4986 | 0.2824 | 0.2975 |
| 1.0124 | 3.52 | 33000 | 1.4959 | 0.2870 | 0.3022 |
| 0.9437 | 3.63 | 34000 | 1.4936 | 0.2837 | 0.2985 |
| 0.9977 | 3.74 | 35000 | 1.4901 | 0.2827 | 0.2976 |
| 1.045 | 3.84 | 36000 | 1.4896 | 0.2836 | 0.2995 |
| 1.0193 | 3.95 | 37000 | 1.4796 | 0.2853 | 0.2968 |
| 0.9276 | 4.06 | 38000 | 1.5135 | 0.2836 | 0.2974 |
| 0.9583 | 4.16 | 39000 | 1.5174 | 0.2825 | 0.2956 |
| 1.0952 | 4.27 | 40000 | 1.5143 | 0.2889 | 0.3026 |
| 1.0029 | 4.38 | 41000 | 1.5124 | 0.2835 | 0.2986 |
| 1.0129 | 4.48 | 42000 | 1.5062 | 0.2858 | 0.3006 |
| 1.012 | 4.59 | 43000 | 1.5096 | 0.2845 | 0.2997 |
| 0.9478 | 4.7 | 44000 | 1.5008 | 0.2853 | 0.3002 |
| 1.0272 | 4.8 | 45000 | 1.5012 | 0.2825 | 0.2958 |
| 1.0532 | 4.91 | 46000 | 1.4957 | 0.2832 | 0.2974 |
| 0.921 | 5.02 | 47000 | 1.5206 | 0.2871 | 0.3009 |
| 0.9246 | 5.12 | 48000 | 1.5259 | 0.2832 | 0.2983 |
| 0.9689 | 5.23 | 49000 | 1.5274 | 0.2842 | 0.2985 |
| 1.127 | 5.34 | 50000 | 1.5186 | 0.2874 | 0.2997 |
| 0.9616 | 5.45 | 51000 | 1.5200 | 0.2839 | 0.2971 |
| 0.9833 | 5.55 | 52000 | 1.5142 | 0.2853 | 0.2980 |
| 0.9113 | 5.66 | 53000 | 1.5172 | 0.2828 | 0.2972 |
| 0.9753 | 5.77 | 54000 | 1.5032 | 0.2829 | 0.2944 |
| 0.9159 | 5.87 | 55000 | 1.4970 | 0.2850 | 0.2987 |
| 1.0663 | 5.98 | 56000 | 1.4979 | 0.2850 | 0.2985 |
| 0.966 | 6.09 | 57000 | 1.5263 | 0.2862 | 0.2976 |
| 0.9234 | 6.19 | 58000 | 1.5342 | 0.2824 | 0.2955 |
| 0.9234 | 6.3 | 59000 | 1.5327 | 0.2838 | 0.2968 |
| 0.972 | 6.41 | 60000 | 1.5291 | 0.2849 | 0.2983 |
| 1.0319 | 6.51 | 61000 | 1.5329 | 0.2836 | 0.2963 |
| 0.9746 | 6.62 | 62000 | 1.5222 | 0.2838 | 0.2972 |
| 1.0719 | 6.73 | 63000 | 1.5249 | 0.2848 | 0.2967 |
| 1.0695 | 6.83 | 64000 | 1.5183 | 0.2882 | 0.3013 |
| 1.0271 | 6.94 | 65000 | 1.5119 | 0.2859 | 0.2982 |
| 1.0119 | 7.05 | 66000 | 1.5410 | 0.2881 | 0.3014 |
| 0.9341 | 7.15 | 67000 | 1.5489 | 0.2871 | 0.2995 |
| 0.9544 | 7.26 | 68000 | 1.5443 | 0.2836 | 0.2974 |
| 1.0198 | 7.37 | 69000 | 1.5401 | 0.2844 | 0.2983 |
| 0.9269 | 7.47 | 70000 | 1.5384 | 0.2823 | 0.2948 |
| 0.9767 | 7.58 | 71000 | 1.5375 | 0.2853 | 0.2987 |
| 0.9861 | 7.69 | 72000 | 1.5282 | 0.2872 | 0.2991 |
| 1.0539 | 7.79 | 73000 | 1.5244 | 0.2854 | 0.2974 |
| 0.8879 | 7.9 | 74000 | 1.5305 | 0.2851 | 0.2989 |
| 0.8739 | 8.01 | 75000 | 1.5454 | 0.2868 | 0.2998 |
| 1.0469 | 8.11 | 76000 | 1.5533 | 0.2908 | 0.3033 |
| 0.9449 | 8.22 | 77000 | 1.5586 | 0.2886 | 0.3007 |
| 0.987 | 8.33 | 78000 | 1.5478 | 0.2900 | 0.3029 |
| 0.9088 | 8.43 | 79000 | 1.5541 | 0.2848 | 0.2976 |
| 0.8866 | 8.54 | 80000 | 1.5456 | 0.2868 | 0.2986 |
| 1.0161 | 8.65 | 81000 | 1.5494 | 0.2889 | 0.3002 |
| 0.982 | 8.76 | 82000 | 1.5388 | 0.2842 | 0.2976 |
| 0.956 | 8.86 | 83000 | 1.5377 | 0.2875 | 0.3010 |
| 0.9267 | 8.97 | 84000 | 1.5415 | 0.2870 | 0.2982 |
| 0.8889 | 9.08 | 85000 | 1.5729 | 0.2855 | 0.2983 |
| 0.9518 | 9.18 | 86000 | 1.5719 | 0.2899 | 0.3030 |
| 0.9469 | 9.29 | 87000 | 1.5661 | 0.2887 | 0.3020 |
| 0.9614 | 9.4 | 88000 | 1.5661 | 0.2938 | 0.3075 |
| 0.9073 | 9.5 | 89000 | 1.5620 | 0.2889 | 0.3021 |
| 0.9808 | 9.61 | 90000 | 1.5706 | 0.2884 | 0.3006 |
| 0.93 | 9.72 | 91000 | 1.5614 | 0.2923 | 0.3056 |
| 0.969 | 9.82 | 92000 | 1.5585 | 0.2891 | 0.3022 |
| 0.912 | 9.93 | 93000 | 1.5562 | 0.2890 | 0.3002 |
| 0.9301 | 10.04 | 94000 | 1.5802 | 0.2910 | 0.3052 |
| 0.9265 | 10.14 | 95000 | 1.5834 | 0.2902 | 0.3030 |
| 0.874 | 10.25 | 96000 | 1.5792 | 0.2898 | 0.3025 |
| 0.877 | 10.36 | 97000 | 1.5813 | 0.2892 | 0.3009 |
| 0.9479 | 10.46 | 98000 | 1.5716 | 0.2859 | 0.2991 |
| 1.1058 | 10.57 | 99000 | 1.5676 | 0.2964 | 0.3088 |
| 0.8595 | 10.68 | 100000 | 1.5734 | 0.2865 | 0.2999 |
| 0.924 | 10.78 | 101000 | 1.5721 | 0.2911 | 0.3024 |
| 0.9618 | 10.89 | 102000 | 1.5665 | 0.2885 | 0.3017 |
| 0.958 | 11.0 | 103000 | 1.5662 | 0.2870 | 0.2998 |
| 0.8141 | 11.1 | 104000 | 1.5915 | 0.2927 | 0.3041 |
| 0.9141 | 11.21 | 105000 | 1.5912 | 0.2952 | 0.3076 |
| 0.8718 | 11.32 | 106000 | 1.5941 | 0.2897 | 0.3034 |
| 0.9337 | 11.42 | 107000 | 1.5873 | 0.2927 | 0.3041 |
| 0.9101 | 11.53 | 108000 | 1.5901 | 0.2931 | 0.3064 |
| 0.9166 | 11.64 | 109000 | 1.5813 | 0.2885 | 0.3021 |
| 0.9369 | 11.74 | 110000 | 1.5768 | 0.2924 | 0.3037 |
| 1.0187 | 11.85 | 111000 | 1.5823 | 0.2884 | 0.2998 |
| 0.8388 | 11.96 | 112000 | 1.5763 | 0.2915 | 0.3041 |
| 0.9172 | 12.06 | 113000 | 1.6043 | 0.2923 | 0.3055 |
| 0.8312 | 12.17 | 114000 | 1.6103 | 0.2950 | 0.3100 |
| 0.8414 | 12.28 | 115000 | 1.6086 | 0.2913 | 0.3030 |
| 0.8941 | 12.39 | 116000 | 1.6057 | 0.2960 | 0.3072 |
| 0.9055 | 12.49 | 117000 | 1.6044 | 0.2905 | 0.3032 |
| 0.9492 | 12.6 | 118000 | 1.6020 | 0.2905 | 0.3030 |
| 0.9831 | 12.71 | 119000 | 1.6003 | 0.2906 | 0.3030 |
| 0.779 | 12.81 | 120000 | 1.6006 | 0.2892 | 0.3029 |
| 0.9086 | 12.92 | 121000 | 1.5959 | 0.2914 | 0.3038 |
| 0.866 | 13.03 | 122000 | 1.6154 | 0.2893 | 0.3045 |
| 0.7936 | 13.13 | 123000 | 1.6200 | 0.2951 | 0.3088 |
| 0.8693 | 13.24 | 124000 | 1.6229 | 0.2934 | 0.3075 |
| 0.8463 | 13.35 | 125000 | 1.6211 | 0.2957 | 0.3092 |
| 0.8946 | 13.45 | 126000 | 1.6192 | 0.2957 | 0.3095 |
| 0.8616 | 13.56 | 127000 | 1.6124 | 0.2928 | 0.3045 |
| 0.88 | 13.67 | 128000 | 1.6147 | 0.2940 | 0.3068 |
| 0.938 | 13.77 | 129000 | 1.6067 | 0.2904 | 0.3055 |
| 0.8949 | 13.88 | 130000 | 1.6065 | 0.2922 | 0.3061 |
| 0.8911 | 13.99 | 131000 | 1.5997 | 0.2896 | 0.3034 |
| 0.813 | 14.09 | 132000 | 1.6342 | 0.2915 | 0.3042 |
| 0.8077 | 14.2 | 133000 | 1.6314 | 0.2926 | 0.3053 |
| 0.8068 | 14.31 | 134000 | 1.6316 | 0.2973 | 0.3106 |
| 0.828 | 14.41 | 135000 | 1.6379 | 0.2899 | 0.3032 |
| 0.9271 | 14.52 | 136000 | 1.6351 | 0.2958 | 0.3095 |
| 0.8314 | 14.63 | 137000 | 1.6294 | 0.2984 | 0.3127 |
| 0.8468 | 14.73 | 138000 | 1.6318 | 0.2951 | 0.3091 |
| 0.8859 | 14.84 | 139000 | 1.6307 | 0.2944 | 0.3068 |
| 0.9133 | 14.95 | 140000 | 1.6172 | 0.2945 | 0.3071 |
| 0.8673 | 15.05 | 141000 | 1.6481 | 0.3022 | 0.3164 |
| 0.7975 | 15.16 | 142000 | 1.6517 | 0.2966 | 0.3091 |
| 0.7824 | 15.27 | 143000 | 1.6588 | 0.2982 | 0.3107 |
| 0.741 | 15.37 | 144000 | 1.6534 | 0.2936 | 0.3079 |
| 0.8285 | 15.48 | 145000 | 1.6464 | 0.2970 | 0.3112 |
| 0.8817 | 15.59 | 146000 | 1.6431 | 0.2955 | 0.3096 |
| 0.807 | 15.7 | 147000 | 1.6395 | 0.3005 | 0.3141 |
| 0.8341 | 15.8 | 148000 | 1.6407 | 0.2956 | 0.3087 |
| 0.8734 | 15.91 | 149000 | 1.6349 | 0.3031 | 0.3158 |
| 0.7333 | 16.02 | 150000 | 1.6556 | 0.2980 | 0.3123 |
| 0.7652 | 16.12 | 151000 | 1.6663 | 0.3046 | 0.3191 |
| 0.8292 | 16.23 | 152000 | 1.6680 | 0.3017 | 0.3165 |
| 0.9419 | 16.34 | 153000 | 1.6651 | 0.3053 | 0.3195 |
| 0.8787 | 16.44 | 154000 | 1.6635 | 0.3008 | 0.3156 |
| 0.9263 | 16.55 | 155000 | 1.6578 | 0.2975 | 0.3100 |
| 0.8244 | 16.66 | 156000 | 1.6601 | 0.2990 | 0.3135 |
| 0.8557 | 16.76 | 157000 | 1.6579 | 0.2949 | 0.3081 |
| 0.8749 | 16.87 | 158000 | 1.6500 | 0.2986 | 0.3121 |
| 0.7865 | 16.98 | 159000 | 1.6466 | 0.3024 | 0.3166 |
| 0.7753 | 17.08 | 160000 | 1.6764 | 0.3046 | 0.3185 |
| 0.8026 | 17.19 | 161000 | 1.6789 | 0.2995 | 0.3133 |
| 0.8223 | 17.3 | 162000 | 1.6821 | 0.3026 | 0.3177 |
| 0.7264 | 17.4 | 163000 | 1.6750 | 0.3035 | 0.3166 |
| 0.8394 | 17.51 | 164000 | 1.6716 | 0.3001 | 0.3139 |
| 0.7711 | 17.62 | 165000 | 1.6698 | 0.3027 | 0.3162 |
| 0.8279 | 17.72 | 166000 | 1.6672 | 0.2968 | 0.3133 |
| 0.8186 | 17.83 | 167000 | 1.6714 | 0.2980 | 0.3123 |
| 0.819 | 17.94 | 168000 | 1.6609 | 0.2991 | 0.3139 |
| 0.7633 | 18.04 | 169000 | 1.6941 | 0.3046 | 0.3181 |
| 0.8339 | 18.15 | 170000 | 1.6916 | 0.3154 | 0.3305 |
| 0.7452 | 18.26 | 171000 | 1.6963 | 0.3085 | 0.3235 |
| 0.8282 | 18.36 | 172000 | 1.7003 | 0.3079 | 0.3220 |
| 0.789 | 18.47 | 173000 | 1.6979 | 0.3037 | 0.3187 |
| 0.8215 | 18.58 | 174000 | 1.6940 | 0.3154 | 0.3297 |
| 0.7891 | 18.68 | 175000 | 1.6897 | 0.3040 | 0.3193 |
| 0.8306 | 18.79 | 176000 | 1.6898 | 0.3045 | 0.3195 |
| 0.8575 | 18.9 | 177000 | 1.6850 | 0.2985 | 0.3142 |
| 0.784 | 19.0 | 178000 | 1.7042 | 0.3013 | 0.3170 |
| 0.7566 | 19.11 | 179000 | 1.7094 | 0.3057 | 0.3223 |
| 0.7863 | 19.22 | 180000 | 1.7119 | 0.3039 | 0.3223 |
| 0.8157 | 19.33 | 181000 | 1.7098 | 0.3116 | 0.3266 |
| 0.8782 | 19.43 | 182000 | 1.7091 | 0.3061 | 0.3232 |
| 0.7863 | 19.54 | 183000 | 1.7071 | 0.3033 | 0.3185 |
| 0.8008 | 19.65 | 184000 | 1.7084 | 0.3077 | 0.3254 |
| 0.7919 | 19.75 | 185000 | 1.6996 | 0.3028 | 0.3204 |
| 0.8435 | 19.86 | 186000 | 1.7012 | 0.3058 | 0.3204 |
| 0.87 | 19.97 | 187000 | 1.7021 | 0.3046 | 0.3201 |
| 0.6902 | 20.07 | 188000 | 1.7367 | 0.2996 | 0.3160 |
| 0.7449 | 20.18 | 189000 | 1.7309 | 0.3119 | 0.3293 |
| 0.8253 | 20.29 | 190000 | 1.7297 | 0.3095 | 0.3254 |
| 0.7288 | 20.39 | 191000 | 1.7273 | 0.3050 | 0.3220 |
| 0.776 | 20.5 | 192000 | 1.7294 | 0.3049 | 0.3228 |
| 0.7632 | 20.61 | 193000 | 1.7238 | 0.3179 | 0.3349 |
| 0.7496 | 20.71 | 194000 | 1.7257 | 0.3107 | 0.3253 |
| 0.8491 | 20.82 | 195000 | 1.7209 | 0.3088 | 0.3253 |
| 0.7668 | 20.93 | 196000 | 1.7177 | 0.3080 | 0.3238 |
| 0.6755 | 21.03 | 197000 | 1.7415 | 0.3087 | 0.3258 |
| 0.6846 | 21.14 | 198000 | 1.7406 | 0.3059 | 0.3218 |
| 0.7551 | 21.25 | 199000 | 1.7416 | 0.3167 | 0.3319 |
| 0.7083 | 21.35 | 200000 | 1.7429 | 0.3120 | 0.3267 |
| 0.7631 | 21.46 | 201000 | 1.7434 | 0.3074 | 0.3240 |
| 0.7814 | 21.57 | 202000 | 1.7408 | 0.3069 | 0.3247 |
| 0.7646 | 21.67 | 203000 | 1.7395 | 0.3109 | 0.3292 |
| 0.7329 | 21.78 | 204000 | 1.7373 | 0.3149 | 0.3331 |
| 0.7393 | 21.89 | 205000 | 1.7377 | 0.3077 | 0.3242 |
| 0.766 | 21.99 | 206000 | 1.7351 | 0.3097 | 0.3259 |
| 0.696 | 22.1 | 207000 | 1.7593 | 0.3116 | 0.3266 |
| 0.8049 | 22.21 | 208000 | 1.7660 | 0.3153 | 0.3340 |
| 0.8011 | 22.31 | 209000 | 1.7611 | 0.3164 | 0.3331 |
| 0.7884 | 22.42 | 210000 | 1.7613 | 0.3127 | 0.3298 |
| 0.7607 | 22.53 | 211000 | 1.7633 | 0.3120 | 0.3300 |
| 0.7159 | 22.64 | 212000 | 1.7603 | 0.3080 | 0.3258 |
| 0.6687 | 22.74 | 213000 | 1.7571 | 0.3120 | 0.3297 |
| 0.7331 | 22.85 | 214000 | 1.7529 | 0.3060 | 0.3227 |
| 0.8109 | 22.96 | 215000 | 1.7582 | 0.3157 | 0.3331 |
| 0.7407 | 23.06 | 216000 | 1.7757 | 0.3191 | 0.3347 |
| 0.69 | 23.17 | 217000 | 1.7770 | 0.3162 | 0.3343 |
| 0.6752 | 23.28 | 218000 | 1.7762 | 0.3255 | 0.3437 |
| 0.7192 | 23.38 | 219000 | 1.7751 | 0.3161 | 0.3337 |
| 0.742 | 23.49 | 220000 | 1.7782 | 0.3136 | 0.3319 |
| 0.7184 | 23.6 | 221000 | 1.7771 | 0.3225 | 0.3398 |
| 0.7296 | 23.7 | 222000 | 1.7796 | 0.3287 | 0.3452 |
| 0.7527 | 23.81 | 223000 | 1.7722 | 0.3172 | 0.3335 |
| 0.7093 | 23.92 | 224000 | 1.7713 | 0.3128 | 0.3306 |
| 0.6691 | 24.02 | 225000 | 1.7913 | 0.3197 | 0.3362 |
| 0.6767 | 24.13 | 226000 | 1.7907 | 0.3153 | 0.3339 |
| 0.7133 | 24.24 | 227000 | 1.7925 | 0.3252 | 0.3436 |
| 0.7094 | 24.34 | 228000 | 1.7910 | 0.3197 | 0.3372 |
| 0.7848 | 24.45 | 229000 | 1.7918 | 0.3263 | 0.3437 |
| 0.7146 | 24.56 | 230000 | 1.7903 | 0.3202 | 0.3375 |
| 0.6932 | 24.66 | 231000 | 1.7895 | 0.3183 | 0.3345 |
| 0.6632 | 24.77 | 232000 | 1.7895 | 0.3193 | 0.3355 |
| 0.7587 | 24.88 | 233000 | 1.7894 | 0.3215 | 0.3387 |
| 0.7197 | 24.98 | 234000 | 1.7863 | 0.3192 | 0.3378 |
| 0.7759 | 25.09 | 235000 | 1.8054 | 0.3178 | 0.3371 |
| 0.6729 | 25.2 | 236000 | 1.8069 | 0.3165 | 0.3335 |
| 0.6144 | 25.3 | 237000 | 1.8059 | 0.3143 | 0.3317 |
| 0.6838 | 25.41 | 238000 | 1.8077 | 0.3183 | 0.3362 |
| 0.6925 | 25.52 | 239000 | 1.8017 | 0.3271 | 0.3432 |
| 0.7301 | 25.62 | 240000 | 1.8035 | 0.3206 | 0.3375 |
| 0.7169 | 25.73 | 241000 | 1.8043 | 0.3198 | 0.3370 |
| 0.6717 | 25.84 | 242000 | 1.8042 | 0.3215 | 0.3364 |
| 0.7113 | 25.94 | 243000 | 1.8048 | 0.3164 | 0.3329 |
| 0.6544 | 26.05 | 244000 | 1.8186 | 0.3225 | 0.3375 |
| 0.6664 | 26.16 | 245000 | 1.8216 | 0.3237 | 0.3403 |
| 0.7131 | 26.27 | 246000 | 1.8234 | 0.3201 | 0.3389 |
| 0.6202 | 26.37 | 247000 | 1.8222 | 0.3193 | 0.3355 |
| 0.7311 | 26.48 | 248000 | 1.8173 | 0.3221 | 0.3383 |
| 0.6377 | 26.59 | 249000 | 1.8200 | 0.3178 | 0.3356 |
| 0.6931 | 26.69 | 250000 | 1.8190 | 0.3251 | 0.3409 |
| 0.6402 | 26.8 | 251000 | 1.8198 | 0.3209 | 0.3385 |
| 0.6492 | 26.91 | 252000 | 1.8188 | 0.3209 | 0.3390 |
| 0.6544 | 27.01 | 253000 | 1.8279 | 0.3212 | 0.3380 |
| 0.641 | 27.12 | 254000 | 1.8321 | 0.3221 | 0.3405 |
| 0.6474 | 27.23 | 255000 | 1.8335 | 0.3230 | 0.3407 |
| 0.6556 | 27.33 | 256000 | 1.8346 | 0.3216 | 0.3395 |
| 0.6917 | 27.44 | 257000 | 1.8332 | 0.3250 | 0.3422 |
| 0.6741 | 27.55 | 258000 | 1.8344 | 0.3228 | 0.3399 |
| 0.6758 | 27.65 | 259000 | 1.8338 | 0.3182 | 0.3362 |
| 0.6579 | 27.76 | 260000 | 1.8353 | 0.3194 | 0.3383 |
| 0.6836 | 27.87 | 261000 | 1.8334 | 0.3235 | 0.3405 |
| 0.6936 | 27.97 | 262000 | 1.8335 | 0.3192 | 0.3391 |
| 0.6588 | 28.08 | 263000 | 1.8444 | 0.3197 | 0.3386 |
| 0.6477 | 28.19 | 264000 | 1.8432 | 0.3189 | 0.3403 |
| 0.6531 | 28.29 | 265000 | 1.8460 | 0.3243 | 0.3422 |
| 0.6467 | 28.4 | 266000 | 1.8448 | 0.3208 | 0.3390 |
| 0.6592 | 28.51 | 267000 | 1.8446 | 0.3234 | 0.3391 |
| 0.5774 | 28.61 | 268000 | 1.8462 | 0.3261 | 0.3446 |
| 0.6444 | 28.72 | 269000 | 1.8468 | 0.3222 | 0.3401 |
| 0.6853 | 28.83 | 270000 | 1.8459 | 0.3224 | 0.3398 |
| 0.6406 | 28.93 | 271000 | 1.8448 | 0.3185 | 0.3362 |
| 0.705 | 29.04 | 272000 | 1.8535 | 0.3243 | 0.3426 |
| 0.6171 | 29.15 | 273000 | 1.8535 | 0.3210 | 0.3398 |
| 0.651 | 29.25 | 274000 | 1.8536 | 0.3263 | 0.3459 |
| 0.6771 | 29.36 | 275000 | 1.8533 | 0.3238 | 0.3426 |
| 0.6554 | 29.47 | 276000 | 1.8536 | 0.3225 | 0.3421 |
| 0.6585 | 29.58 | 277000 | 1.8523 | 0.3212 | 0.3410 |
| 0.5784 | 29.68 | 278000 | 1.8531 | 0.3187 | 0.3379 |
| 0.6542 | 29.79 | 279000 | 1.8530 | 0.3242 | 0.3430 |
| 0.6015 | 29.9 | 280000 | 1.8528 | 0.3241 | 0.3436 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1+cu116
- Datasets 2.14.4
- Tokenizers 0.13.3
|
him009/bloomz_fine_tuned_marketing_email | him009 | 2023-09-16T11:45:40Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-16T11:45:39Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
bongo2112/sdxl-db-harmonize-headshot | bongo2112 | 2023-09-16T11:43:38Z | 3 | 1 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
]
| text-to-image | 2023-09-16T11:41:34Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of kondeharmo man
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
dariadaria/reviews_classifier | dariadaria | 2023-09-16T11:43:10Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-08-05T02:59:44Z | SENTIMENT_LABELS = {
'NEGATIVE': 0,
'POSITIVE': 1,
'NEUTRAL': 2,
}
num_classes = 3
---
language:
- en
thumbnail: "url to a thumbnail used in social sharing"
tags:
- sentiment analysis
license: commercial use disallowed
datasets:
- dariadaria/disneyland_reviews
metrics:
confusion matrix
| | pred:0(NEG)| pred:1(POS)| pred:2(NEU)|
| ------------ | --------- | ----------- | ----------- |
| true:0(NEG) | 793| 188| 171|
| true:1(POS) | 227 | 920| 260|
| true:2(NEU) | 115 | 203| 5087|
--- |
CyberHarem/fukiyose_seiri_toarumajutsunoindex | CyberHarem | 2023-09-16T11:40:55Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/fukiyose_seiri_toarumajutsunoindex",
"license:mit",
"region:us"
]
| text-to-image | 2023-08-15T22:22:23Z | ---
license: mit
datasets:
- CyberHarem/fukiyose_seiri_toarumajutsunoindex
pipeline_tag: text-to-image
tags:
- art
---
# Lora of fukiyose_seiri_toarumajutsunoindex
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5100, you need to download `5100/fukiyose_seiri_toarumajutsunoindex.pt` as the embedding and `5100/fukiyose_seiri_toarumajutsunoindex.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 5100**, with the score of 0.907. The trigger words are:
1. `fukiyose_seiri_toarumajutsunoindex`
2. `black_hair, long_hair, brown_eyes`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **5100** | **0.907** | [**Download**](5100/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5100/previews/pattern_12.png) | [<NSFW, click to see>](5100/previews/bikini.png) | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.847 | [Download](4760/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4760/previews/pattern_12.png) | [<NSFW, click to see>](4760/previews/bikini.png) | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.862 | [Download](4420/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4420/previews/pattern_12.png) | [<NSFW, click to see>](4420/previews/bikini.png) | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| 4080 | 0.839 | [Download](4080/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4080/previews/pattern_12.png) | [<NSFW, click to see>](4080/previews/bikini.png) | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.820 | [Download](3740/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3740/previews/pattern_12.png) | [<NSFW, click to see>](3740/previews/bikini.png) | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.875 | [Download](3400/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3400/previews/pattern_12.png) | [<NSFW, click to see>](3400/previews/bikini.png) | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.831 | [Download](3060/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3060/previews/pattern_12.png) | [<NSFW, click to see>](3060/previews/bikini.png) | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.790 | [Download](2720/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2720/previews/pattern_12.png) | [<NSFW, click to see>](2720/previews/bikini.png) | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.777 | [Download](2380/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2380/previews/pattern_12.png) | [<NSFW, click to see>](2380/previews/bikini.png) | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.789 | [Download](2040/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2040/previews/pattern_12.png) | [<NSFW, click to see>](2040/previews/bikini.png) | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.810 | [Download](1700/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1700/previews/pattern_12.png) | [<NSFW, click to see>](1700/previews/bikini.png) | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.756 | [Download](1360/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1360/previews/pattern_12.png) | [<NSFW, click to see>](1360/previews/bikini.png) | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.770 | [Download](1020/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1020/previews/pattern_12.png) | [<NSFW, click to see>](1020/previews/bikini.png) | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.670 | [Download](680/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](680/previews/pattern_12.png) | [<NSFW, click to see>](680/previews/bikini.png) | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.689 | [Download](340/fukiyose_seiri_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](340/previews/pattern_12.png) | [<NSFW, click to see>](340/previews/bikini.png) | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
raffel-22/emotion_classification_2 | raffel-22 | 2023-09-16T11:35:36Z | 217 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-16T11:19:27Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_classification_2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.51875
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# emotion_classification_2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3274
- Accuracy: 0.5188
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 20 | 1.9337 | 0.3563 |
| No log | 2.0 | 40 | 1.7116 | 0.3375 |
| No log | 3.0 | 60 | 1.5755 | 0.4562 |
| No log | 4.0 | 80 | 1.4939 | 0.45 |
| No log | 5.0 | 100 | 1.4377 | 0.5062 |
| No log | 6.0 | 120 | 1.4363 | 0.4562 |
| No log | 7.0 | 140 | 1.3615 | 0.5125 |
| No log | 8.0 | 160 | 1.3021 | 0.5375 |
| No log | 9.0 | 180 | 1.3307 | 0.525 |
| No log | 10.0 | 200 | 1.3085 | 0.4938 |
| No log | 11.0 | 220 | 1.2798 | 0.5813 |
| No log | 12.0 | 240 | 1.2707 | 0.525 |
| No log | 13.0 | 260 | 1.2339 | 0.55 |
| No log | 14.0 | 280 | 1.3053 | 0.5437 |
| No log | 15.0 | 300 | 1.3038 | 0.4938 |
| No log | 16.0 | 320 | 1.3088 | 0.5375 |
| No log | 17.0 | 340 | 1.3336 | 0.5312 |
| No log | 18.0 | 360 | 1.3053 | 0.5 |
| No log | 19.0 | 380 | 1.2206 | 0.5687 |
| No log | 20.0 | 400 | 1.2598 | 0.5312 |
| No log | 21.0 | 420 | 1.3332 | 0.5125 |
| No log | 22.0 | 440 | 1.3388 | 0.5312 |
| No log | 23.0 | 460 | 1.3129 | 0.5563 |
| No log | 24.0 | 480 | 1.3632 | 0.5062 |
| 0.9153 | 25.0 | 500 | 1.4166 | 0.4688 |
| 0.9153 | 26.0 | 520 | 1.4094 | 0.5 |
| 0.9153 | 27.0 | 540 | 1.4294 | 0.475 |
| 0.9153 | 28.0 | 560 | 1.4937 | 0.475 |
| 0.9153 | 29.0 | 580 | 1.3897 | 0.4938 |
| 0.9153 | 30.0 | 600 | 1.4565 | 0.475 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
CyberHarem/sakuma_mayu_idolmastercinderellagirls | CyberHarem | 2023-09-16T11:23:00Z | 0 | 1 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/sakuma_mayu_idolmastercinderellagirls",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-16T11:07:34Z | ---
license: mit
datasets:
- CyberHarem/sakuma_mayu_idolmastercinderellagirls
pipeline_tag: text-to-image
tags:
- art
---
# Lora of sakuma_mayu_idolmastercinderellagirls
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 7500, you need to download `7500/sakuma_mayu_idolmastercinderellagirls.pt` as the embedding and `7500/sakuma_mayu_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 7500**, with the score of 0.963. The trigger words are:
1. `sakuma_mayu_idolmastercinderellagirls`
2. `brown_hair, blush, blue_eyes, smile, hairband, ribbon, bangs, heart, jewelry, short_hair, bow, earrings, breasts`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:---------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:----------------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **7500** | **0.963** | [**Download**](7500/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](7500/previews/pattern_7.png) |  | [<NSFW, click to see>](7500/previews/bikini.png) | [<NSFW, click to see>](7500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7500/previews/nude.png) | [<NSFW, click to see>](7500/previews/nude2.png) |  |  |
| 7000 | 0.961 | [Download](7000/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](7000/previews/pattern_7.png) |  | [<NSFW, click to see>](7000/previews/bikini.png) | [<NSFW, click to see>](7000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7000/previews/nude.png) | [<NSFW, click to see>](7000/previews/nude2.png) |  |  |
| 6500 | 0.958 | [Download](6500/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](6500/previews/pattern_7.png) |  | [<NSFW, click to see>](6500/previews/bikini.png) | [<NSFW, click to see>](6500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6500/previews/nude.png) | [<NSFW, click to see>](6500/previews/nude2.png) |  |  |
| 6000 | 0.939 | [Download](6000/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](6000/previews/pattern_7.png) |  | [<NSFW, click to see>](6000/previews/bikini.png) | [<NSFW, click to see>](6000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6000/previews/nude.png) | [<NSFW, click to see>](6000/previews/nude2.png) |  |  |
| 5500 | 0.930 | [Download](5500/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5500/previews/pattern_7.png) |  | [<NSFW, click to see>](5500/previews/bikini.png) | [<NSFW, click to see>](5500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5500/previews/nude.png) | [<NSFW, click to see>](5500/previews/nude2.png) |  |  |
| 5000 | 0.948 | [Download](5000/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5000/previews/pattern_7.png) |  | [<NSFW, click to see>](5000/previews/bikini.png) | [<NSFW, click to see>](5000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5000/previews/nude.png) | [<NSFW, click to see>](5000/previews/nude2.png) |  |  |
| 4500 | 0.933 | [Download](4500/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4500/previews/pattern_7.png) |  | [<NSFW, click to see>](4500/previews/bikini.png) | [<NSFW, click to see>](4500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4500/previews/nude.png) | [<NSFW, click to see>](4500/previews/nude2.png) |  |  |
| 4000 | 0.952 | [Download](4000/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4000/previews/pattern_7.png) |  | [<NSFW, click to see>](4000/previews/bikini.png) | [<NSFW, click to see>](4000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4000/previews/nude.png) | [<NSFW, click to see>](4000/previews/nude2.png) |  |  |
| 3500 | 0.936 | [Download](3500/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3500/previews/pattern_7.png) |  | [<NSFW, click to see>](3500/previews/bikini.png) | [<NSFW, click to see>](3500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3500/previews/nude.png) | [<NSFW, click to see>](3500/previews/nude2.png) |  |  |
| 3000 | 0.925 | [Download](3000/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3000/previews/pattern_7.png) |  | [<NSFW, click to see>](3000/previews/bikini.png) | [<NSFW, click to see>](3000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3000/previews/nude.png) | [<NSFW, click to see>](3000/previews/nude2.png) |  |  |
| 2500 | 0.930 | [Download](2500/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2500/previews/pattern_7.png) |  | [<NSFW, click to see>](2500/previews/bikini.png) | [<NSFW, click to see>](2500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2500/previews/nude.png) | [<NSFW, click to see>](2500/previews/nude2.png) |  |  |
| 2000 | 0.889 | [Download](2000/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2000/previews/pattern_7.png) |  | [<NSFW, click to see>](2000/previews/bikini.png) | [<NSFW, click to see>](2000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2000/previews/nude.png) | [<NSFW, click to see>](2000/previews/nude2.png) |  |  |
| 1500 | 0.865 | [Download](1500/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1500/previews/pattern_7.png) |  | [<NSFW, click to see>](1500/previews/bikini.png) | [<NSFW, click to see>](1500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [<NSFW, click to see>](1500/previews/nude2.png) |  |  |
| 1000 | 0.873 | [Download](1000/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1000/previews/pattern_7.png) |  | [<NSFW, click to see>](1000/previews/bikini.png) | [<NSFW, click to see>](1000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [<NSFW, click to see>](1000/previews/nude2.png) |  |  |
| 500 | 0.882 | [Download](500/sakuma_mayu_idolmastercinderellagirls.zip) |  |  |  |  |  |  | [<NSFW, click to see>](500/previews/pattern_7.png) |  | [<NSFW, click to see>](500/previews/bikini.png) | [<NSFW, click to see>](500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](500/previews/nude.png) | [<NSFW, click to see>](500/previews/nude2.png) |  |  |
|
samkas125/bert-large-legal-sentence-classification | samkas125 | 2023-09-16T11:20:32Z | 202 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-16T07:37:20Z | ---
license: mit
---
# bert-large-legal-sentence-classification
## Model Description
**bert-large-legal-sentence-classification** is a finetuned `bert-large-cased` model that is ready to use for legal sentence classification into rhetorical roles. It was fine-tuned using a repository of [analyzed disability-claim decisions](https://github.com/vernrwalker/VetClaims-JSON/) issued by the Board of Veterans' Appeals ("BVA") of the U.S. Department of Veterans Affairs.
### How to use
#### With HuggingFace pipeline
```python
from transformers import AutoTokenizer, BertForSequenceClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("samkas125/bert-large-legal-sentence-classification")
model = BertForSequenceClassification.from_pretrained('samkas125/bert-large-legal-sentence-classification')
nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
example = "The Veteran did not have a psychiatric disorder in service that was unrelated to the use of drugs."
results = nlp(example)
print(results)
```
#### Without HuggingFace pipeline
```python
from transformers import AutoTokenizer, BertForSequenceClassification
from torch import softmax, argmax
tokenizer = AutoTokenizer.from_pretrained("samkas125/bert-large-legal-sentence-classification")
model = BertForSequenceClassification.from_pretrained('samkas125/bert-large-legal-sentence-classification')
sentence = "The Veteran did not have a psychiatric disorder in service that was unrelated to the use of drugs."
encoded_input = tokenizer(sentence, return_tensors='pt')
output = model(**encoded_input)
logits = output.logits
probs = softmax(logits, dim=1)
predicted_class = argmax(probs, dim=1).item()
print(predicted_class)
```
Check the `config.json` file to map the index of a class to its name.
### Limitations and bias
This model is limited by the size of its training set of 6153 sentences. Additionally, it was only trained on disability claim decisions issued by the U.S. Department of Veterans Affairs. It may not generalize well to non-legal sentences, or sentences from other types of cases within the legal domain.
## Training data
A repository of [analyzed disability-claim decisions](https://github.com/vernrwalker/VetClaims-JSON/) issued by the Board of Veterans' Appeals ("BVA") of the U.S. Department of Veterans Affairs was used to finetune the `bert-large-cased` model for sequence classification. It consisted of 6153 sentences from 50 cases. The sentences were classified by the Research Laboratory for Law, Logic and Technology (LLT Lab) at the Maurice A. Deane School of Law at Hofstra University, in New York. The analysis consists of classifying sentences in those decisions according to their rhetorical role (as defined within the same repository).
|
CDS-GROUP/graph-classification | CDS-GROUP | 2023-09-16T11:16:29Z | 38 | 0 | transformers | [
"transformers",
"pytorch",
"graphormer",
"generated_from_trainer",
"base_model:clefourrier/graphormer-base-pcqm4mv2",
"base_model:finetune:clefourrier/graphormer-base-pcqm4mv2",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2023-09-16T11:16:17Z | ---
license: mit
base_model: clefourrier/graphormer-base-pcqm4mv2
tags:
- generated_from_trainer
model-index:
- name: graph-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# graph-classification
This model is a fine-tuned version of [clefourrier/graphormer-base-pcqm4mv2](https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 1280
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
judy93536/distilroberta-pr200k-ep20 | judy93536 | 2023-09-16T11:08:22Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-09-15T12:50:35Z | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-pr200k-ep20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-pr200k-ep20
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0749
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.3e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.19
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.8844 | 1.0 | 5480 | 1.6711 |
| 1.6262 | 2.0 | 10960 | 1.4794 |
| 1.5005 | 3.0 | 16440 | 1.3796 |
| 1.4432 | 4.0 | 21920 | 1.3313 |
| 1.3904 | 5.0 | 27400 | 1.2843 |
| 1.3378 | 6.0 | 32880 | 1.2441 |
| 1.3057 | 7.0 | 38360 | 1.2149 |
| 1.2781 | 8.0 | 43840 | 1.1927 |
| 1.2497 | 9.0 | 49320 | 1.1780 |
| 1.2219 | 10.0 | 54800 | 1.1626 |
| 1.2068 | 11.0 | 60280 | 1.1500 |
| 1.1832 | 12.0 | 65760 | 1.1368 |
| 1.1789 | 13.0 | 71240 | 1.1230 |
| 1.1571 | 14.0 | 76720 | 1.1135 |
| 1.1439 | 15.0 | 82200 | 1.1020 |
| 1.1419 | 16.0 | 87680 | 1.0954 |
| 1.1247 | 17.0 | 93160 | 1.0893 |
| 1.1159 | 18.0 | 98640 | 1.0806 |
| 1.1119 | 19.0 | 104120 | 1.0776 |
| 1.0978 | 20.0 | 109600 | 1.0740 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
CyberHarem/uiharu_kazari_toarumajutsunoindex | CyberHarem | 2023-09-16T10:53:02Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/uiharu_kazari_toarumajutsunoindex",
"license:mit",
"region:us"
]
| text-to-image | 2023-08-15T21:30:58Z | ---
license: mit
datasets:
- CyberHarem/uiharu_kazari_toarumajutsunoindex
pipeline_tag: text-to-image
tags:
- art
---
# Lora of uiharu_kazari_toarumajutsunoindex
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 4080, you need to download `4080/uiharu_kazari_toarumajutsunoindex.pt` as the embedding and `4080/uiharu_kazari_toarumajutsunoindex.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 4080**, with the score of 0.974. The trigger words are:
1. `uiharu_kazari_toarumajutsunoindex`
2. `short_hair, black_hair, hair_ornament, flower, hair_flower, head_wreath, serafuku, brown_eyes`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-----------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 5100 | 0.955 | [Download](5100/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](5100/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5100/previews/nude.png) | [<NSFW, click to see>](5100/previews/nude2.png) |  |  |
| 4760 | 0.946 | [Download](4760/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4760/previews/nude.png) | [<NSFW, click to see>](4760/previews/nude2.png) |  |  |
| 4420 | 0.969 | [Download](4420/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4420/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4420/previews/nude.png) | [<NSFW, click to see>](4420/previews/nude2.png) |  |  |
| **4080** | **0.974** | [**Download**](4080/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](4080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4080/previews/nude.png) | [<NSFW, click to see>](4080/previews/nude2.png) |  |  |
| 3740 | 0.858 | [Download](3740/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3740/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3740/previews/nude.png) | [<NSFW, click to see>](3740/previews/nude2.png) |  |  |
| 3400 | 0.886 | [Download](3400/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3400/previews/nude.png) | [<NSFW, click to see>](3400/previews/nude2.png) |  |  |
| 3060 | 0.865 | [Download](3060/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](3060/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3060/previews/nude.png) | [<NSFW, click to see>](3060/previews/nude2.png) |  |  |
| 2720 | 0.895 | [Download](2720/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2720/previews/nude.png) | [<NSFW, click to see>](2720/previews/nude2.png) |  |  |
| 2380 | 0.924 | [Download](2380/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2380/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2380/previews/nude.png) | [<NSFW, click to see>](2380/previews/nude2.png) |  |  |
| 2040 | 0.929 | [Download](2040/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](2040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2040/previews/nude.png) | [<NSFW, click to see>](2040/previews/nude2.png) |  |  |
| 1700 | 0.843 | [Download](1700/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1700/previews/nude.png) | [<NSFW, click to see>](1700/previews/nude2.png) |  |  |
| 1360 | 0.806 | [Download](1360/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1360/previews/nude.png) | [<NSFW, click to see>](1360/previews/nude2.png) |  |  |
| 1020 | 0.795 | [Download](1020/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](1020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1020/previews/nude.png) | [<NSFW, click to see>](1020/previews/nude2.png) |  |  |
| 680 | 0.694 | [Download](680/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](680/previews/nude.png) | [<NSFW, click to see>](680/previews/nude2.png) |  |  |
| 340 | 0.614 | [Download](340/uiharu_kazari_toarumajutsunoindex.zip) |  |  |  |  |  |  | [<NSFW, click to see>](340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](340/previews/nude.png) | [<NSFW, click to see>](340/previews/nude2.png) |  |  |
|
MUmairAB/python-code-generator | MUmairAB | 2023-09-16T10:20:02Z | 78 | 1 | transformers | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-07-27T10:23:04Z | ---
license: mit
tags:
- generated_from_keras_callback
base_model: gpt2
model-index:
- name: MUmairAB/python-code-generator
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MUmairAB/python-code-generator
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.4439
- Validation Loss: 2.6055
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 20785, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.4439 | 2.6055 | 0 |
| 2.4438 | 2.6055 | 1 |
| 2.4439 | 2.6055 | 2 |
| 2.4437 | 2.6055 | 3 |
| 2.4439 | 2.6055 | 4 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
CyberHarem/ogata_chieri_idolmastercinderellagirls | CyberHarem | 2023-09-16T10:15:40Z | 0 | 1 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/ogata_chieri_idolmastercinderellagirls",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-16T09:58:20Z | ---
license: mit
datasets:
- CyberHarem/ogata_chieri_idolmastercinderellagirls
pipeline_tag: text-to-image
tags:
- art
---
# Lora of ogata_chieri_idolmastercinderellagirls
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6240, you need to download `6240/ogata_chieri_idolmastercinderellagirls.pt` as the embedding and `6240/ogata_chieri_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6240**, with the score of 0.916. The trigger words are:
1. `ogata_chieri_idolmastercinderellagirls`
2. `brown_hair, twintails, brown_eyes, blush, smile, bangs, open_mouth, hair_ornament`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7800 | 0.903 | [Download](7800/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/pattern_11.png) | [<NSFW, click to see>](7800/previews/bikini.png) | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7280 | 0.889 | [Download](7280/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7280/previews/pattern_11.png) | [<NSFW, click to see>](7280/previews/bikini.png) | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| 6760 | 0.884 | [Download](6760/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6760/previews/pattern_11.png) | [<NSFW, click to see>](6760/previews/bikini.png) | [<NSFW, click to see>](6760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| **6240** | **0.916** | [**Download**](6240/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/pattern_11.png) | [<NSFW, click to see>](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5720 | 0.900 | [Download](5720/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/pattern_11.png) | [<NSFW, click to see>](5720/previews/bikini.png) | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5200 | 0.907 | [Download](5200/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/pattern_11.png) | [<NSFW, click to see>](5200/previews/bikini.png) | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.883 | [Download](4680/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4680/previews/pattern_11.png) | [<NSFW, click to see>](4680/previews/bikini.png) | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 4160 | 0.914 | [Download](4160/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4160/previews/pattern_11.png) | [<NSFW, click to see>](4160/previews/bikini.png) | [<NSFW, click to see>](4160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| 3640 | 0.841 | [Download](3640/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3640/previews/pattern_11.png) | [<NSFW, click to see>](3640/previews/bikini.png) | [<NSFW, click to see>](3640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| 3120 | 0.872 | [Download](3120/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3120/previews/pattern_11.png) | [<NSFW, click to see>](3120/previews/bikini.png) | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.834 | [Download](2600/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2600/previews/pattern_11.png) | [<NSFW, click to see>](2600/previews/bikini.png) | [<NSFW, click to see>](2600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.823 | [Download](2080/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2080/previews/pattern_11.png) | [<NSFW, click to see>](2080/previews/bikini.png) | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.746 | [Download](1560/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1560/previews/pattern_11.png) | [<NSFW, click to see>](1560/previews/bikini.png) | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.617 | [Download](1040/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1040/previews/pattern_11.png) | [<NSFW, click to see>](1040/previews/bikini.png) | [<NSFW, click to see>](1040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.411 | [Download](520/ogata_chieri_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](520/previews/pattern_11.png) | [<NSFW, click to see>](520/previews/bikini.png) | [<NSFW, click to see>](520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  |
|
facebook/dinov2-small-imagenet1k-1-layer | facebook | 2023-09-16T10:14:58Z | 5,029 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"dinov2",
"image-classification",
"dino",
"vision",
"dataset:imagenet-1k",
"arxiv:2304.07193",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-14T19:57:42Z | ---
license: apache-2.0
tags:
- dino
- vision
datasets:
- imagenet-1k
---
# Vision Transformer (small-sized model) trained using DINOv2
Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Oquab et al. and first released in [this repository](https://github.com/facebookresearch/dinov2).
Disclaimer: The team releasing DINOv2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion.
Images are presented to the model as a sequence of fixed-size patches, which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not include any fine-tuned heads.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the model for classifying an image among one of the [1000 ImageNet labels](https://huggingface.co/datasets/huggingface/label-files/blob/main/imagenet-1k-id2label.json). See the [model hub](https://huggingface.co/models?search=facebook/dinov2) to look for
other fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = AutoImageProcessor.from_pretrained('facebook/dinov2-small-imagenet1k-1-layer')
model = AutoModelForImageClassification.from_pretrained('facebook/dinov2-small-imagenet1k-1-layer')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
### BibTeX entry and citation info
```bibtex
misc{oquab2023dinov2,
title={DINOv2: Learning Robust Visual Features without Supervision},
author={Maxime Oquab and Timothée Darcet and Théo Moutakanni and Huy Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel Haziza and Francisco Massa and Alaaeldin El-Nouby and Mahmoud Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Hervé Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski},
year={2023},
eprint={2304.07193},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
facebook/dinov2-giant-imagenet1k-1-layer | facebook | 2023-09-16T10:14:37Z | 6,734 | 3 | transformers | [
"transformers",
"pytorch",
"safetensors",
"dinov2",
"image-classification",
"dino",
"vision",
"dataset:imagenet-1k",
"arxiv:2304.07193",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-14T20:18:41Z | ---
license: apache-2.0
tags:
- dino
- vision
datasets:
- imagenet-1k
---
# Vision Transformer (giant-sized model) trained using DINOv2
Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Oquab et al. and first released in [this repository](https://github.com/facebookresearch/dinov2).
Disclaimer: The team releasing DINOv2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion.
Images are presented to the model as a sequence of fixed-size patches, which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder.
Note that this model does not include any fine-tuned heads.
By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image.
## Intended uses & limitations
You can use the model for classifying an image among one of the [1000 ImageNet labels](https://huggingface.co/datasets/huggingface/label-files/blob/main/imagenet-1k-id2label.json). See the [model hub](https://huggingface.co/models?search=facebook/dinov2) to look for
other fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model:
```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
processor = AutoImageProcessor.from_pretrained('facebook/dinov2-giant-imagenet1k-1-layer')
model = AutoModelForImageClassification.from_pretrained('facebook/dinov2-giant-imagenet1k-1-layer')
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
### BibTeX entry and citation info
```bibtex
misc{oquab2023dinov2,
title={DINOv2: Learning Robust Visual Features without Supervision},
author={Maxime Oquab and Timothée Darcet and Théo Moutakanni and Huy Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel Haziza and Francisco Massa and Alaaeldin El-Nouby and Mahmoud Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Hervé Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski},
year={2023},
eprint={2304.07193},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |
CyberHarem/index_toarumajutsunoindex | CyberHarem | 2023-09-16T10:10:24Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/index_toarumajutsunoindex",
"license:mit",
"region:us"
]
| text-to-image | 2023-08-15T19:18:09Z | ---
license: mit
datasets:
- CyberHarem/index_toarumajutsunoindex
pipeline_tag: text-to-image
tags:
- art
---
# Lora of index_toarumajutsunoindex
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 11700, you need to download `11700/index_toarumajutsunoindex.pt` as the embedding and `11700/index_toarumajutsunoindex.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 11700**, with the score of 0.916. The trigger words are:
1. `index_toarumajutsunoindex`
2. `long_hair, habit, nun, safety_pin, robe, green_eyes, grey_hair, blue_hair`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | pattern_17 | pattern_18 | pattern_19 | pattern_20 | pattern_21 | pattern_22 | pattern_23 | pattern_24 | pattern_25 | pattern_26 | pattern_27 | pattern_28 | pattern_29 | pattern_30 | pattern_31 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:----------|:----------|:----------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:-------------------------------------------|:---------------------------------------------------|:---------------------------------------|:---------------------------------------|:---------------------------------------|:------------------------------------------------|:-------------------------------------------------|:---------------------------------------|:-------------------------------------------|
| **11700** | **0.916** | [**Download**](11700/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](11700/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](11700/previews/bondage.png) |  |  |  | [<NSFW, click to see>](11700/previews/nude.png) | [<NSFW, click to see>](11700/previews/nude2.png) |  |  |
| 10920 | 0.909 | [Download](10920/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](10920/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](10920/previews/bondage.png) |  |  |  | [<NSFW, click to see>](10920/previews/nude.png) | [<NSFW, click to see>](10920/previews/nude2.png) |  |  |
| 10140 | 0.842 | [Download](10140/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](10140/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](10140/previews/bondage.png) |  |  |  | [<NSFW, click to see>](10140/previews/nude.png) | [<NSFW, click to see>](10140/previews/nude2.png) |  |  |
| 9360 | 0.906 | [Download](9360/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](9360/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](9360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](9360/previews/nude.png) | [<NSFW, click to see>](9360/previews/nude2.png) |  |  |
| 8580 | 0.896 | [Download](8580/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](8580/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](8580/previews/bondage.png) |  |  |  | [<NSFW, click to see>](8580/previews/nude.png) | [<NSFW, click to see>](8580/previews/nude2.png) |  |  |
| 7800 | 0.829 | [Download](7800/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](7800/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7020 | 0.820 | [Download](7020/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](7020/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7020/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7020/previews/nude.png) | [<NSFW, click to see>](7020/previews/nude2.png) |  |  |
| 6240 | 0.800 | [Download](6240/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](6240/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5460 | 0.823 | [Download](5460/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](5460/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5460/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5460/previews/nude.png) | [<NSFW, click to see>](5460/previews/nude2.png) |  |  |
| 4680 | 0.848 | [Download](4680/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](4680/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 3900 | 0.847 | [Download](3900/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](3900/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3900/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3900/previews/nude.png) | [<NSFW, click to see>](3900/previews/nude2.png) |  |  |
| 3120 | 0.755 | [Download](3120/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](3120/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2340 | 0.826 | [Download](2340/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](2340/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2340/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2340/previews/nude.png) | [<NSFW, click to see>](2340/previews/nude2.png) |  |  |
| 1560 | 0.806 | [Download](1560/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](1560/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 780 | 0.579 | [Download](780/index_toarumajutsunoindex.zip) |  | [<NSFW, click to see>](780/previews/pattern_2.png) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](780/previews/bondage.png) |  |  |  | [<NSFW, click to see>](780/previews/nude.png) | [<NSFW, click to see>](780/previews/nude2.png) |  |  |
|
paras1/dog-images-in-different-backgrounds | paras1 | 2023-09-16T09:41:58Z | 4 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-09-16T09:36:20Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Dog-images-in-different-backgrounds Dreambooth model trained by paras1 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: NCU86
Sample pictures of this concept:
|
Shishir1807/CT_M2 | Shishir1807 | 2023-09-16T09:41:37Z | 164 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-09-16T09:41:01Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/CT_M2",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/CT_M2",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/CT_M2",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/CT_M2" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
ceviriyap/bert_5 | ceviriyap | 2023-09-16T09:33:52Z | 46 | 0 | transformers | [
"transformers",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
]
| null | 2023-09-16T09:26:17Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: bert_5
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert_5
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 7.7984
- Validation Loss: 8.7049
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 8.4283 | 8.6635 | 0 |
| 7.9611 | 8.5577 | 1 |
| 7.8389 | 8.6501 | 2 |
| 7.7437 | 8.6738 | 3 |
| 7.7984 | 8.7049 | 4 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.10.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Shishir1807/CT_M6 | Shishir1807 | 2023-09-16T09:30:25Z | 168 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-09-16T09:29:49Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/CT_M6",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/CT_M6",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/CT_M6",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/CT_M6" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
Shishir1807/CT_M5 | Shishir1807 | 2023-09-16T09:26:26Z | 165 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-09-16T09:25:51Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/CT_M5",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/CT_M5",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/CT_M5",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/CT_M5" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
c-g/ppo-Huggy | c-g | 2023-09-16T09:21:02Z | 18 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-09-16T09:20:43Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: c-g/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
CyberHarem/ootsuki_yui_idolmastercinderellagirls | CyberHarem | 2023-09-16T09:05:06Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/ootsuki_yui_idolmastercinderellagirls",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-16T08:43:01Z | ---
license: mit
datasets:
- CyberHarem/ootsuki_yui_idolmastercinderellagirls
pipeline_tag: text-to-image
tags:
- art
---
# Lora of ootsuki_yui_idolmastercinderellagirls
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 6760, you need to download `6760/ootsuki_yui_idolmastercinderellagirls.pt` as the embedding and `6760/ootsuki_yui_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 6760**, with the score of 0.945. The trigger words are:
1. `ootsuki_yui_idolmastercinderellagirls`
2. `blonde_hair, long_hair, blue_eyes, smile, blush, bangs, breasts, open_mouth, wavy_hair, collarbone, jewelry, teeth, large_breasts, hair_ornament`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:---------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7800 | 0.937 | [Download](7800/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7800/previews/pattern_15.png) |  | [<NSFW, click to see>](7800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7800/previews/nude.png) | [<NSFW, click to see>](7800/previews/nude2.png) |  |  |
| 7280 | 0.943 | [Download](7280/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7280/previews/pattern_15.png) |  | [<NSFW, click to see>](7280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7280/previews/nude.png) | [<NSFW, click to see>](7280/previews/nude2.png) |  |  |
| **6760** | **0.945** | [**Download**](6760/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6760/previews/pattern_15.png) |  | [<NSFW, click to see>](6760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6760/previews/nude.png) | [<NSFW, click to see>](6760/previews/nude2.png) |  |  |
| 6240 | 0.884 | [Download](6240/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/pattern_15.png) |  | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5720 | 0.892 | [Download](5720/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/pattern_15.png) |  | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5200 | 0.911 | [Download](5200/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5200/previews/pattern_15.png) |  | [<NSFW, click to see>](5200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5200/previews/nude.png) | [<NSFW, click to see>](5200/previews/nude2.png) |  |  |
| 4680 | 0.902 | [Download](4680/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4680/previews/pattern_15.png) |  | [<NSFW, click to see>](4680/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4680/previews/nude.png) | [<NSFW, click to see>](4680/previews/nude2.png) |  |  |
| 4160 | 0.833 | [Download](4160/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4160/previews/pattern_15.png) |  | [<NSFW, click to see>](4160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4160/previews/nude.png) | [<NSFW, click to see>](4160/previews/nude2.png) |  |  |
| 3640 | 0.885 | [Download](3640/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3640/previews/pattern_15.png) |  | [<NSFW, click to see>](3640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3640/previews/nude.png) | [<NSFW, click to see>](3640/previews/nude2.png) |  |  |
| 3120 | 0.882 | [Download](3120/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3120/previews/pattern_15.png) |  | [<NSFW, click to see>](3120/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3120/previews/nude.png) | [<NSFW, click to see>](3120/previews/nude2.png) |  |  |
| 2600 | 0.902 | [Download](2600/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2600/previews/pattern_15.png) |  | [<NSFW, click to see>](2600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2600/previews/nude.png) | [<NSFW, click to see>](2600/previews/nude2.png) |  |  |
| 2080 | 0.899 | [Download](2080/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2080/previews/pattern_15.png) |  | [<NSFW, click to see>](2080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2080/previews/nude.png) | [<NSFW, click to see>](2080/previews/nude2.png) |  |  |
| 1560 | 0.901 | [Download](1560/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1560/previews/pattern_15.png) |  | [<NSFW, click to see>](1560/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1560/previews/nude.png) | [<NSFW, click to see>](1560/previews/nude2.png) |  |  |
| 1040 | 0.895 | [Download](1040/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1040/previews/pattern_15.png) |  | [<NSFW, click to see>](1040/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1040/previews/nude.png) | [<NSFW, click to see>](1040/previews/nude2.png) |  |  |
| 520 | 0.778 | [Download](520/ootsuki_yui_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](520/previews/pattern_15.png) |  | [<NSFW, click to see>](520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](520/previews/nude.png) | [<NSFW, click to see>](520/previews/nude2.png) |  |  |
|
Doctor-Shotgun/CalliopeDS-L2-13B-exl2 | Doctor-Shotgun | 2023-09-16T09:04:14Z | 10 | 2 | transformers | [
"transformers",
"llama",
"text-generation",
"llama-2",
"en",
"license:agpl-3.0",
"autotrain_compatible",
"region:us"
]
| text-generation | 2023-09-16T02:43:51Z | ---
inference: false
language:
- en
pipeline_tag: text-generation
tags:
- llama
- llama-2
license: agpl-3.0
---
# CalliopeDS-L2-13B-exl2
Exllama v2 quant of [Doctor-Shotgun/CalliopeDS-L2-13B](https://huggingface.co/Doctor-Shotgun/CalliopeDS-L2-13B)
Branches:
- main: 4 decoder bits per weight, 6 head bits
- ideal for 12gb GPUs, or 16gb GPUs with NTK extended context or CFG
- 6.0bpw-h6: 6 decoder bits per weight, 6 head bits
- ideal for 16gb GPUs, or 24gb GPUs with NTK extended context or CFG
- 8bit-32g-h8: all tensors 8bit 32g, 8 head bits
- experimental quant, this is with exllamav2 monkeypatched to quantize all tensors to 8bit 32g
- similar in size to old GPTQ 8bit no groupsize, recommend 24gb GPU
- maxbpw-h8: ???bpw, 8 head bits
- experimental quant, this is the maximum optimized mixed quant size that the current version of exllamav2 produces
- somewhat larger than 6.0bpw but not as large as 8bit, recommend 24gb GPU |
Shishir1807/CT_M4 | Shishir1807 | 2023-09-16T09:01:56Z | 164 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-09-16T09:01:16Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [EleutherAI/pythia-2.8b-deduped](https://huggingface.co/EleutherAI/pythia-2.8b-deduped)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install einops==0.6.1
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="Shishir1807/CT_M4",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Shishir1807/CT_M4",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"Shishir1807/CT_M4",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Shishir1807/CT_M4" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<|endoftext|><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GPTNeoXForCausalLM(
(gpt_neox): GPTNeoXModel(
(embed_in): Embedding(50304, 2560)
(layers): ModuleList(
(0-31): 32 x GPTNeoXLayer(
(input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
(attention): GPTNeoXAttention(
(rotary_emb): RotaryEmbedding()
(query_key_value): Linear(in_features=2560, out_features=7680, bias=True)
(dense): Linear(in_features=2560, out_features=2560, bias=True)
)
(mlp): GPTNeoXMLP(
(dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)
(dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)
(act): GELUActivation()
)
)
)
(final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)
)
(embed_out): Linear(in_features=2560, out_features=50304, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
kishanmurthy/bloom-3b-finetuned-squad-v2 | kishanmurthy | 2023-09-16T08:47:55Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-16T06:55:13Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
CyberHarem/shirai_kuroko_toarumajutsunoindex | CyberHarem | 2023-09-16T08:36:42Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/shirai_kuroko_toarumajutsunoindex",
"license:mit",
"region:us"
]
| text-to-image | 2023-08-15T18:41:30Z | ---
license: mit
datasets:
- CyberHarem/shirai_kuroko_toarumajutsunoindex
pipeline_tag: text-to-image
tags:
- art
---
# Lora of shirai_kuroko_toarumajutsunoindex
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 7200, you need to download `7200/shirai_kuroko_toarumajutsunoindex.pt` as the embedding and `7200/shirai_kuroko_toarumajutsunoindex.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 7200**, with the score of 0.932. The trigger words are:
1. `shirai_kuroko_toarumajutsunoindex`
2. `twintails, brown_hair, long_hair, bow, hair_bow, brown_eyes`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-----------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| **7200** | **0.932** | [**Download**](7200/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7200/previews/nude.png) | [<NSFW, click to see>](7200/previews/nude2.png) |  |  |
| 6720 | 0.857 | [Download](6720/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6720/previews/nude.png) | [<NSFW, click to see>](6720/previews/nude2.png) |  |  |
| 6240 | 0.879 | [Download](6240/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| 5760 | 0.890 | [Download](5760/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5760/previews/nude.png) | [<NSFW, click to see>](5760/previews/nude2.png) |  |  |
| 5280 | 0.851 | [Download](5280/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) |  |  |
| 4800 | 0.904 | [Download](4800/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) |  |  |
| 4320 | 0.884 | [Download](4320/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) |  |  |
| 3840 | 0.869 | [Download](3840/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3840/previews/nude.png) | [<NSFW, click to see>](3840/previews/nude2.png) |  |  |
| 3360 | 0.890 | [Download](3360/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3360/previews/nude.png) | [<NSFW, click to see>](3360/previews/nude2.png) |  |  |
| 2880 | 0.854 | [Download](2880/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2880/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2880/previews/nude.png) | [<NSFW, click to see>](2880/previews/nude2.png) |  |  |
| 2400 | 0.881 | [Download](2400/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) |  |  |
| 1920 | 0.757 | [Download](1920/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1920/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) |  |  |
| 1440 | 0.790 | [Download](1440/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1440/previews/nude.png) | [<NSFW, click to see>](1440/previews/nude2.png) |  |  |
| 960 | 0.569 | [Download](960/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](960/previews/nude.png) | [<NSFW, click to see>](960/previews/nude2.png) |  |  |
| 480 | 0.451 | [Download](480/shirai_kuroko_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](480/previews/bondage.png) |  |  |  | [<NSFW, click to see>](480/previews/nude.png) | [<NSFW, click to see>](480/previews/nude2.png) |  |  |
|
mhasnanr/image_classification | mhasnanr | 2023-09-16T08:27:57Z | 216 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-16T08:27:17Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.525
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2966
- Accuracy: 0.525
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.4307 | 0.475 |
| No log | 2.0 | 80 | 1.3231 | 0.5125 |
| No log | 3.0 | 120 | 1.3044 | 0.5437 |
| No log | 4.0 | 160 | 1.3204 | 0.525 |
| No log | 5.0 | 200 | 1.2457 | 0.5875 |
| No log | 6.0 | 240 | 1.3604 | 0.5125 |
| No log | 7.0 | 280 | 1.2296 | 0.5813 |
| No log | 8.0 | 320 | 1.3598 | 0.525 |
| No log | 9.0 | 360 | 1.3343 | 0.5188 |
| No log | 10.0 | 400 | 1.4003 | 0.5625 |
| No log | 11.0 | 440 | 1.3580 | 0.5563 |
| No log | 12.0 | 480 | 1.3214 | 0.5687 |
| 0.4908 | 13.0 | 520 | 1.3713 | 0.5312 |
| 0.4908 | 14.0 | 560 | 1.3820 | 0.55 |
| 0.4908 | 15.0 | 600 | 1.3384 | 0.5813 |
| 0.4908 | 16.0 | 640 | 1.4905 | 0.5375 |
| 0.4908 | 17.0 | 680 | 1.3985 | 0.5687 |
| 0.4908 | 18.0 | 720 | 1.4733 | 0.5312 |
| 0.4908 | 19.0 | 760 | 1.3403 | 0.5813 |
| 0.4908 | 20.0 | 800 | 1.3991 | 0.5563 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
pralaypati/stable-diffusion-fine-tuned | pralaypati | 2023-09-16T08:25:10Z | 30 | 0 | diffusers | [
"diffusers",
"safetensors",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-09-16T06:52:18Z | ---
license: apache-2.0
---
A sample fine-tuned version of a pre-trained stable diffusion model. Model has been fine-tuned on two subjects and the corresponding prompts. |
aapoorv75/whisper-large-v2-hindi-for-yes-no-100steps | aapoorv75 | 2023-09-16T07:43:41Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-16T07:43:27Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
Henok21/test_trainer | Henok21 | 2023-09-16T07:38:40Z | 30 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-08-28T15:44:30Z | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: test_trainer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7351
- eval_accuracy: 0.7076
- eval_runtime: 63.312
- eval_samples_per_second: 31.605
- eval_steps_per_second: 1.99
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
minhbui/viettel_v1_mix_100k | minhbui | 2023-09-16T07:19:29Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"vi",
"en",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-09-16T03:51:46Z | ---
license: llama2
language:
- vi
- en
---
Our model finetune qlora on 100k samples (50k dolphins data + 43k webglm data + 10k squad paraphrases answer). All the data is translated into Vietnamese. |
om-ashish-soni/pos-ner-tagging-v4 | om-ashish-soni | 2023-09-16T07:16:33Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:om-ashish-soni/pos-ner-tagging-v3",
"base_model:finetune:om-ashish-soni/pos-ner-tagging-v3",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-09-16T06:15:47Z | ---
license: apache-2.0
base_model: om-ashish-soni/pos-ner-tagging-v3
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pos-ner-tagging-v4
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9242642437694764
- name: Recall
type: recall
value: 0.9264290339671514
- name: F1
type: f1
value: 0.9244306954134476
- name: Accuracy
type: accuracy
value: 0.9264290339671514
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pos-ner-tagging-v4
This model is a fine-tuned version of [om-ashish-soni/pos-ner-tagging-v3](https://huggingface.co/om-ashish-soni/pos-ner-tagging-v3) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6642
- Precision: 0.9243
- Recall: 0.9264
- F1: 0.9244
- Accuracy: 0.9264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.98 | 54 | 0.6425 | 0.9254 | 0.9272 | 0.9250 | 0.9272 |
| No log | 1.99 | 109 | 0.6624 | 0.9239 | 0.9261 | 0.9240 | 0.9261 |
| No log | 2.99 | 164 | 0.6593 | 0.9245 | 0.9267 | 0.9245 | 0.9267 |
| No log | 3.99 | 219 | 0.6608 | 0.9251 | 0.9270 | 0.9250 | 0.9270 |
| No log | 4.99 | 274 | 0.6698 | 0.9246 | 0.9269 | 0.9245 | 0.9269 |
| No log | 6.0 | 329 | 0.6648 | 0.9246 | 0.9264 | 0.9244 | 0.9264 |
| No log | 7.0 | 384 | 0.6651 | 0.9244 | 0.9266 | 0.9245 | 0.9266 |
| No log | 7.87 | 432 | 0.6642 | 0.9243 | 0.9264 | 0.9244 | 0.9264 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ngeg2015/bert-base-banking77-pt2 | ngeg2015 | 2023-09-16T07:13:58Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:banking77",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-16T06:46:28Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- f1
model-index:
- name: bert-base-banking77-pt2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: test[0:10]
args: default
metrics:
- name: F1
type: f1
value: 1.0
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-banking77-pt2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the banking77 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0297
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 4 | 3.0563 | 0.8889 |
| No log | 2.0 | 8 | 2.3672 | 1.0 |
| No log | 3.0 | 12 | 2.0297 | 1.0 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
cedricsarigumba/llama2-qlora-finetunined-french | cedricsarigumba | 2023-09-16T07:12:55Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-16T07:12:37Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
espnet/eason_gigaspeech_train_asr2_e_branchformer12_lr_raw_wavlm_large_21_km1000 | espnet | 2023-09-16T06:51:40Z | 0 | 0 | espnet | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:gigaspeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
]
| automatic-speech-recognition | 2023-09-16T06:42:05Z | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- gigaspeech
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/eason_gigaspeech_train_asr2_e_branchformer12_lr_raw_wavlm_large_21_km1000`
This model was trained by yichenl5 using gigaspeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout 6296b27ed648631ce3aa63182d9b3b8230b5e34e
pip install -e .
cd egs2/gigaspeech/asr2
./run.sh --skip_data_prep false --skip_train true --download_model espnet/eason_gigaspeech_train_asr2_e_branchformer12_lr_raw_wavlm_large_21_km1000
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sat Sep 9 03:24:28 EDT 2023`
- python version: `3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0]`
- espnet version: `espnet 202308`
- pytorch version: `pytorch 1.13.1+cu117`
- Git hash: `83e687f3b41310a000f4a5b65857734709752bf6`
- Commit date: `Tue Aug 15 18:31:02 2023 -0400`
## exp/asr_train_discrete_asr_e_branchformer1_e12_lr_raw_wavlm_large_21_km1000_bpe_rm6000_bpe_ts3000
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/test|19930|392325|89.0|6.9|4.1|1.9|12.9|73.4|
|decode_ctc0.3_asr_model_valid.acc.best/test|19930|392325|88.8|6.8|4.3|1.9|13.1|74.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/test|19930|2056231|95.1|1.5|3.4|1.5|6.4|73.4|
|decode_ctc0.3_asr_model_valid.acc.best/test|19930|2056231|94.9|1.5|3.6|1.5|6.6|74.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/test|19930|527751|88.8|5.5|5.7|1.7|13.0|73.4|
|decode_ctc0.3_asr_model_valid.acc.best/test|19930|527751|88.5|5.5|6.0|1.7|13.2|74.5|
## exp/asr_train_discrete_asr_e_branchformer1_e12_lr_raw_wavlm_large_21_km1000_bpe_rm6000_bpe_ts3000/decode_ctc0.3_asr_model_valid.acc.best
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|org/dev|5715|129240|89.4|6.2|4.4|2.3|12.9|79.5|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|org/dev|5715|673778|95.4|1.3|3.3|1.8|6.4|79.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|org/dev|5715|170948|89.2|5.0|5.7|1.9|12.7|79.5|
## exp/asr_train_discrete_asr_e_branchformer1_e12_lr_raw_wavlm_large_21_km1000_bpe_rm6000_bpe_ts3000/decode_ctc0.3_asr_model_valid.acc.ave
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|org/dev|5715|129240|89.5|6.3|4.2|2.4|12.9|78.8|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|org/dev|5715|673778|95.5|1.3|3.2|1.9|6.3|78.8|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|org/dev|5715|170948|89.5|5.1|5.5|2.0|12.5|78.8|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_discrete_asr_e_branchformer1_e12_lr.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/asr_train_discrete_asr_e_branchformer1_e12_lr_raw_wavlm_large_21_km1000_bpe_rm6000_bpe_ts3000
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 1
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 38805
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 15
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: 1000
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 50000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe6000_bpe3000/train/text_shape.bpe
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe6000_bpe3000/train/src_text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe6000_bpe3000/valid/text_shape.bpe
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe6000_bpe3000/valid/src_text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 150
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
train_data_path_and_name_and_type:
- - dump/raw/train/text.ts.en
- text
- text
- - dump/raw/train/text.rm.wavlm_large_21_km1000
- src_text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/text.ts.en
- text
- text
- - dump/raw/dev/text.rm.wavlm_large_21_km1000
- src_text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
token_list:
- <blank>
- <unk>
- S
- ▁THE
- ▁TO
- ▁A
- ▁OF
- ▁AND
- ''''
- ▁THAT
- ▁IN
- ▁I
- ▁YOU
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- ▁IT
- ED
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- ING
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- ▁WAS
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- ▁FOR
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- Y
- M
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- ▁NOT
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- ER
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- E
- ▁AT
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- ▁ME
- ▁ABOUT
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- ▁YOUR
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- ▁PEOPLE
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- O
- VE
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- ▁MORE
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- C
- ▁SOME
- P
- ▁TIME
- ▁GET
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- ▁GOING
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- ▁HIM
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- ▁ALSO
- ▁KIND
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- ▁MUCH
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- ▁WELL
- ▁GOOD
- I
- ▁DID
- ▁WORK
- ERS
- ▁FIRST
- ▁THAN
- ▁LITTLE
- CE
- AN
- ▁LOT
- ▁NEW
- ▁MAN
- ▁EVEN
- ▁DOWN
- ▁UN
- ▁ANY
- U
- ▁GOT
- ▁SOMETHING
- ▁F
- IC
- LO
- ▁THING
- ▁GREAT
- ▁TAKE
- ITY
- ABLE
- ▁SHOULD
- NE
- TER
- ▁COME
- IT
- ENT
- LA
- ▁ONLY
- 'ON'
- ▁ACTUALLY
- MENT
- ▁LOOK
- RY
- ▁BEING
- ▁NEED
- ▁THROUGH
- ▁MOST
- RO
- ▁PART
- KE
- RA
- ▁WORLD
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- ▁MANY
- H
- IVE
- ▁CO
- ▁LIFE
- ▁YEARS
- ▁UM
- ▁DAY
- DE
- ▁MEAN
- ATE
- ▁YEAH
- ▁SHOW
- ▁BEFORE
- TE
- EN
- UR
- ▁THREE
- ET
- ▁OWN
- ▁MA
- ▁DIFFERENT
- IST
- ▁MIGHT
- US
- ▁MADE
- EST
- ▁LET
- ▁PO
- Z
- IR
- ▁AGAIN
- ▁B
- ▁PLACE
- ▁LOVE
- ▁AFTER
- ▁POINT
- ▁USE
- OL
- ▁HELP
- NESS
- ▁CON
- ▁NEVER
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- ▁OFF
- ▁STILL
- ▁END
- ▁MAY
- ▁PUT
- ▁PRO
- X
- ▁SAME
- ▁EVERY
- ▁LA
- ▁RA
- ▁DIS
- ▁BA
- SH
- AT
- NA
- ▁TOO
- ▁G
- ANT
- UL
- ▁THOUGHT
- TI
- ▁GIVE
- ▁LAST
- MA
- ▁DOING
- AGE
- ▁OLD
- TA
- ▁AROUND
- ▁CAME
- ▁SORT
- ME
- IES
- ▁BO
- ▁P
- IS
- ▁O
- ▁FIND
- ▁WHY
- ▁TALK
- AS
- ▁FIVE
- ▁TELL
- ▁EX
- ▁GRA
- VI
- TIC
- IA
- ▁YEAR
- ▁AM
- ▁PRE
- ▁ALWAYS
- ▁FEEL
- ▁DIDN
- ▁CA
- ▁D
- ▁MO
- ▁FOUR
- ▁QUESTION
- ▁BIT
- ▁ANOTHER
- ATED
- ▁SUCH
- ANCE
- ▁THOUSAND
- ▁TA
- ▁UNDER
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- ▁MAXIM
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- ▁CROWN
- ▁SUICIDE
- ▁SCREAM
- '0000001'
- ▁EVOLVE
- ▁ENEMIES
- ▁TWEET
- ▁VICTORIA
- ▁COMBINATION
- ▁PROVISION
- ▁ABANDON
- ▁EXAMINE
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- ▁AMENDMENT
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- ▁SIGH
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- ▁DESCEND
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- ▁RACIAL
- ▁FORMULA
- ▁IMMEDIATE
- ▁EXPOSED
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- ▁DISTRESS
- ▁COOPER
- ▁PROPHET
- ▁ALTOGETHER
- ▁SUBSTANCE
- ▁HORROR
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token_type: bpe
src_token_type: bpe
bpemodel: data/token_list/tgt_bpe_unigram3000_ts_en/bpe.model
src_bpemodel: data/token_list/src_bpe_unigram6000_rm_wavlm_large_21_km1000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
tokenizer_encode_conf: null
src_tokenizer_encode_conf:
enable_sampling: true
alpha: 0.4
nbest_size: -1
frontend: embed
frontend_conf:
embed_dim: 512
positional_dropout_rate: 0.1
specaug: specaug
specaug_conf:
apply_time_warp: false
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: false
freq_mask_width_range:
- 0
- 10
num_freq_mask: 0
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 10
preencoder: null
preencoder_conf: {}
encoder: e_branchformer
encoder_conf:
output_size: 256
attention_heads: 4
attention_layer_type: rel_selfattn
pos_enc_layer_type: rel_pos
rel_pos_type: latest
cgmlp_linear_units: 1024
cgmlp_conv_kernel: 31
use_linear_after_conv: false
gate_activation: identity
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv1d2
layer_drop_rate: 0.0
linear_units: 1024
positionwise_layer_type: linear
use_ffn: true
macaron_ffn: true
merge_conv_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
layer_drop_rate: 0.0
model: discrete_asr
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
share_decoder_input_output_embed: false
share_encoder_decoder_input_embed: false
required:
- output_dir
- src_token_list
- token_list
version: '202308'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
flatmoon102/image_classification | flatmoon102 | 2023-09-16T06:46:36Z | 221 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-16T06:29:33Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: image_classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.45625
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image_classification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4303
- Accuracy: 0.4562
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.4403 | 0.45 |
| No log | 2.0 | 80 | 1.4300 | 0.4313 |
| No log | 3.0 | 120 | 1.3902 | 0.5 |
| No log | 4.0 | 160 | 1.3475 | 0.4688 |
| No log | 5.0 | 200 | 1.3698 | 0.4938 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
ceviriyap/bert_4 | ceviriyap | 2023-09-16T06:33:15Z | 47 | 0 | transformers | [
"transformers",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
]
| null | 2023-09-16T05:18:30Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: bert_4
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert_4
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Epoch: 39
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 9.1922 | 0 |
| nan | 1 |
| nan | 2 |
| nan | 3 |
| nan | 4 |
| nan | 5 |
| nan | 6 |
| nan | 7 |
| nan | 8 |
| nan | 9 |
| nan | 10 |
| nan | 11 |
| nan | 12 |
| nan | 13 |
| nan | 14 |
| nan | 15 |
| nan | 16 |
| nan | 17 |
| nan | 18 |
| nan | 19 |
| nan | 20 |
| nan | 21 |
| nan | 22 |
| nan | 23 |
| nan | 24 |
| nan | 25 |
| nan | 26 |
| nan | 27 |
| nan | 28 |
| nan | 29 |
| nan | 30 |
| nan | 31 |
| nan | 32 |
| nan | 33 |
| nan | 34 |
| nan | 35 |
| nan | 36 |
| nan | 37 |
| nan | 38 |
| nan | 39 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.10.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Sandra26/vit-model | Sandra26 | 2023-09-16T06:30:25Z | 217 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2023-09-13T20:09:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0468
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1624 | 3.85 | 500 | 0.0468 | 0.9925 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
shantanudave/shantanuimagessept10 | shantanudave | 2023-09-16T06:23:42Z | 1 | 2 | diffusers | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
]
| text-to-image | 2023-09-16T06:18:49Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a sdaveshantanu
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
VictorGil75/Modelo_Clasificacion_Taller_NoTaller | VictorGil75 | 2023-09-16T06:21:37Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-09-16T06:21:28Z | # Modelo_Clasificacion_Taller_NoTaller
Modelo de clasificación para identificar si una revisión pertenece a un Taller o no.
Este modelo fue entrenado para clasificar reseñas en la categoría 'Taller' o 'No Taller'. |
tensor-diffusion/chilloutmix-NI | tensor-diffusion | 2023-09-16T06:18:07Z | 352 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"text-to-image",
"DiffusionPipeline",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-09-16T05:57:17Z | ---
license: openrail++
pipeline_tag: text-to-image
tags:
- stable-diffusion
- text-to-image
- diffusers
- DiffusionPipeline
library_name: diffusers
--- |
CyberHarem/itsuwa_toarumajutsunoindex | CyberHarem | 2023-09-16T06:07:04Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/itsuwa_toarumajutsunoindex",
"license:mit",
"region:us"
]
| text-to-image | 2023-08-15T21:57:11Z | ---
license: mit
datasets:
- CyberHarem/itsuwa_toarumajutsunoindex
pipeline_tag: text-to-image
tags:
- art
---
# Lora of itsuwa_toarumajutsunoindex
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5760, you need to download `5760/itsuwa_toarumajutsunoindex.pt` as the embedding and `5760/itsuwa_toarumajutsunoindex.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 5760**, with the score of 0.930. The trigger words are:
1. `itsuwa_toarumajutsunoindex`
2. `brown_eyes, brown_hair, short_hair`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | pattern_12 | pattern_13 | pattern_14 | pattern_15 | pattern_16 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:----------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7200 | 0.917 | [Download](7200/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7200/previews/pattern_16.png) | [<NSFW, click to see>](7200/previews/bikini.png) | [<NSFW, click to see>](7200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7200/previews/nude.png) | [<NSFW, click to see>](7200/previews/nude2.png) |  |  |
| 6720 | 0.905 | [Download](6720/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6720/previews/pattern_16.png) | [<NSFW, click to see>](6720/previews/bikini.png) | [<NSFW, click to see>](6720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6720/previews/nude.png) | [<NSFW, click to see>](6720/previews/nude2.png) |  |  |
| 6240 | 0.914 | [Download](6240/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6240/previews/pattern_16.png) | [<NSFW, click to see>](6240/previews/bikini.png) | [<NSFW, click to see>](6240/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6240/previews/nude.png) | [<NSFW, click to see>](6240/previews/nude2.png) |  |  |
| **5760** | **0.930** | [**Download**](5760/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5760/previews/pattern_16.png) | [<NSFW, click to see>](5760/previews/bikini.png) | [<NSFW, click to see>](5760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5760/previews/nude.png) | [<NSFW, click to see>](5760/previews/nude2.png) |  |  |
| 5280 | 0.912 | [Download](5280/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5280/previews/pattern_16.png) | [<NSFW, click to see>](5280/previews/bikini.png) | [<NSFW, click to see>](5280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) |  |  |
| 4800 | 0.829 | [Download](4800/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4800/previews/pattern_16.png) | [<NSFW, click to see>](4800/previews/bikini.png) | [<NSFW, click to see>](4800/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4800/previews/nude.png) | [<NSFW, click to see>](4800/previews/nude2.png) |  |  |
| 4320 | 0.868 | [Download](4320/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4320/previews/pattern_16.png) | [<NSFW, click to see>](4320/previews/bikini.png) | [<NSFW, click to see>](4320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4320/previews/nude.png) | [<NSFW, click to see>](4320/previews/nude2.png) |  |  |
| 3840 | 0.834 | [Download](3840/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3840/previews/pattern_16.png) | [<NSFW, click to see>](3840/previews/bikini.png) | [<NSFW, click to see>](3840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3840/previews/nude.png) | [<NSFW, click to see>](3840/previews/nude2.png) |  |  |
| 3360 | 0.821 | [Download](3360/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3360/previews/pattern_16.png) | [<NSFW, click to see>](3360/previews/bikini.png) | [<NSFW, click to see>](3360/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3360/previews/nude.png) | [<NSFW, click to see>](3360/previews/nude2.png) |  |  |
| 2880 | 0.685 | [Download](2880/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2880/previews/pattern_16.png) | [<NSFW, click to see>](2880/previews/bikini.png) | [<NSFW, click to see>](2880/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2880/previews/nude.png) | [<NSFW, click to see>](2880/previews/nude2.png) |  |  |
| 2400 | 0.798 | [Download](2400/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2400/previews/pattern_16.png) | [<NSFW, click to see>](2400/previews/bikini.png) | [<NSFW, click to see>](2400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2400/previews/nude.png) | [<NSFW, click to see>](2400/previews/nude2.png) |  |  |
| 1920 | 0.723 | [Download](1920/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1920/previews/pattern_16.png) | [<NSFW, click to see>](1920/previews/bikini.png) | [<NSFW, click to see>](1920/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1920/previews/nude.png) | [<NSFW, click to see>](1920/previews/nude2.png) |  |  |
| 1440 | 0.823 | [Download](1440/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1440/previews/pattern_16.png) | [<NSFW, click to see>](1440/previews/bikini.png) | [<NSFW, click to see>](1440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1440/previews/nude.png) | [<NSFW, click to see>](1440/previews/nude2.png) |  |  |
| 960 | 0.544 | [Download](960/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](960/previews/pattern_16.png) | [<NSFW, click to see>](960/previews/bikini.png) | [<NSFW, click to see>](960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](960/previews/nude.png) | [<NSFW, click to see>](960/previews/nude2.png) |  |  |
| 480 | 0.322 | [Download](480/itsuwa_toarumajutsunoindex.zip) |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](480/previews/pattern_16.png) | [<NSFW, click to see>](480/previews/bikini.png) | [<NSFW, click to see>](480/previews/bondage.png) |  |  |  | [<NSFW, click to see>](480/previews/nude.png) | [<NSFW, click to see>](480/previews/nude2.png) |  |  |
|
MouseTrap/StyleGen-Loopster-DL | MouseTrap | 2023-09-16T05:57:46Z | 2 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:riffusion/riffusion-model-v1",
"base_model:adapter:riffusion/riffusion-model-v1",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-09-16T05:50:05Z |
---
license: creativeml-openrail-m
base_model: riffusion/riffusion-model-v1
instance_prompt: Loopster style
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - MouseTrap/StyleGen-Looper
These are LoRA adaption weights for riffusion/riffusion-model-v1. The weights were trained on Loopster style using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
codecompletedeployment/st-codesearch-distilroberta-base | codecompletedeployment | 2023-09-16T05:14:13Z | 2 | 1 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"dataset:code_search_net",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2023-09-15T20:48:43Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- code_search_net
---
# flax-sentence-embeddings/st-codesearch-distilroberta-base
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
It was trained on the [code_search_net](https://huggingface.co/datasets/code_search_net) dataset and can be used to search program code given text.
## Usage:
```python
from sentence_transformers import SentenceTransformer, util
#This list the defines the different programm codes
code = ["""def sort_list(x):
return sorted(x)""",
"""def count_above_threshold(elements, threshold=0):
counter = 0
for e in elements:
if e > threshold:
counter += 1
return counter""",
"""def find_min_max(elements):
min_ele = 99999
max_ele = -99999
for e in elements:
if e < min_ele:
min_ele = e
if e > max_ele:
max_ele = e
return min_ele, max_ele"""]
model = SentenceTransformer("flax-sentence-embeddings/st-codesearch-distilroberta-base")
# Encode our code into the vector space
code_emb = model.encode(code, convert_to_tensor=True)
# Interactive demo: Enter queries, and the method returns the best function from the
# 3 functions we defined
while True:
query = input("Query: ")
query_emb = model.encode(query, convert_to_tensor=True)
hits = util.semantic_search(query_emb, code_emb)[0]
top_hit = hits[0]
print("Cossim: {:.2f}".format(top_hit['score']))
print(code[top_hit['corpus_id']])
print("\n\n")
```
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('flax-sentence-embeddings/st-codesearch-distilroberta-base')
embeddings = model.encode(sentences)
print(embeddings)
```
## Training
The model was trained with a DistilRoBERTa-base model for 10k training steps on the codesearch dataset with batch_size 256 and MultipleNegativesRankingLoss.
It is some preliminary model. It was neither tested nor was the trained quite sophisticated
The model was trained with the parameters:
**DataLoader**:
`MultiDatasetDataLoader.MultiDatasetDataLoader` of length 5371 with parameters:
```
{'batch_size': 256}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20, 'similarity_fct': 'dot_score'}
```
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "warmupconstant",
"steps_per_epoch": 10000,
"warmup_steps": 500,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
ceviriyap/bert_3 | ceviriyap | 2023-09-16T05:06:01Z | 46 | 0 | transformers | [
"transformers",
"tf",
"bert",
"pretraining",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
]
| null | 2023-09-16T04:58:25Z | ---
tags:
- generated_from_keras_callback
model-index:
- name: bert_3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert_3
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.0851
- Epoch: 19
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 1e-04, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 8.8352 | 0 |
| 6.8732 | 1 |
| 6.6390 | 2 |
| 6.3008 | 3 |
| 6.5051 | 4 |
| 6.3542 | 5 |
| 6.2322 | 6 |
| 6.2831 | 7 |
| 6.3018 | 8 |
| 6.2346 | 9 |
| 6.1593 | 10 |
| 6.1451 | 11 |
| 6.1091 | 12 |
| 6.1909 | 13 |
| 6.1473 | 14 |
| 6.0694 | 15 |
| 5.9517 | 16 |
| 6.1890 | 17 |
| 6.1535 | 18 |
| 6.0851 | 19 |
### Framework versions
- Transformers 4.33.1
- TensorFlow 2.10.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
dislikename/sd-class-butterflies-32 | dislikename | 2023-09-16T04:47:58Z | 44 | 0 | diffusers | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2023-09-16T04:47:52Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('dislikename/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
brenth82/timmer | brenth82 | 2023-09-16T04:47:16Z | 0 | 0 | tensorflowtts | [
"tensorflowtts",
"audio-to-audio",
"license:artistic-2.0",
"region:us"
]
| audio-to-audio | 2023-09-16T04:22:09Z | ---
pipeline_tag: audio-to-audio
license: artistic-2.0
library_name: tensorflowtts
--- |
nightdude/config_8113572 | nightdude | 2023-09-16T04:35:17Z | 2 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-09-16T04:33:11Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0.dev0
|
CyberHarem/koshimizu_sachiko_idolmastercinderellagirls | CyberHarem | 2023-09-16T04:10:40Z | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/koshimizu_sachiko_idolmastercinderellagirls",
"license:mit",
"region:us"
]
| text-to-image | 2023-09-16T03:54:52Z | ---
license: mit
datasets:
- CyberHarem/koshimizu_sachiko_idolmastercinderellagirls
pipeline_tag: text-to-image
tags:
- art
---
# Lora of koshimizu_sachiko_idolmastercinderellagirls
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 7000, you need to download `7000/koshimizu_sachiko_idolmastercinderellagirls.pt` as the embedding and `7000/koshimizu_sachiko_idolmastercinderellagirls.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 7000**, with the score of 0.992. The trigger words are:
1. `koshimizu_sachiko_idolmastercinderellagirls`
2. `purple_hair, short_hair, brown_eyes, smile, blush, hair_ornament, open_mouth, hairclip`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | pattern_11 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:---------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 7500 | 0.969 | [Download](7500/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7500/previews/bikini.png) | [<NSFW, click to see>](7500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7500/previews/nude.png) | [<NSFW, click to see>](7500/previews/nude2.png) |  |  |
| **7000** | **0.992** | [**Download**](7000/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](7000/previews/bikini.png) | [<NSFW, click to see>](7000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](7000/previews/nude.png) | [<NSFW, click to see>](7000/previews/nude2.png) |  |  |
| 6500 | 0.990 | [Download](6500/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6500/previews/bikini.png) | [<NSFW, click to see>](6500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6500/previews/nude.png) | [<NSFW, click to see>](6500/previews/nude2.png) |  |  |
| 6000 | 0.947 | [Download](6000/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6000/previews/bikini.png) | [<NSFW, click to see>](6000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6000/previews/nude.png) | [<NSFW, click to see>](6000/previews/nude2.png) |  |  |
| 5500 | 0.992 | [Download](5500/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5500/previews/bikini.png) | [<NSFW, click to see>](5500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5500/previews/nude.png) | [<NSFW, click to see>](5500/previews/nude2.png) |  |  |
| 5000 | 0.992 | [Download](5000/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5000/previews/bikini.png) | [<NSFW, click to see>](5000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5000/previews/nude.png) | [<NSFW, click to see>](5000/previews/nude2.png) |  |  |
| 4500 | 0.992 | [Download](4500/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4500/previews/bikini.png) | [<NSFW, click to see>](4500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4500/previews/nude.png) | [<NSFW, click to see>](4500/previews/nude2.png) |  |  |
| 4000 | 0.965 | [Download](4000/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4000/previews/bikini.png) | [<NSFW, click to see>](4000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4000/previews/nude.png) | [<NSFW, click to see>](4000/previews/nude2.png) |  |  |
| 3500 | 0.950 | [Download](3500/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3500/previews/bikini.png) | [<NSFW, click to see>](3500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3500/previews/nude.png) | [<NSFW, click to see>](3500/previews/nude2.png) |  |  |
| 3000 | 0.991 | [Download](3000/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3000/previews/bikini.png) | [<NSFW, click to see>](3000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3000/previews/nude.png) | [<NSFW, click to see>](3000/previews/nude2.png) |  |  |
| 2500 | 0.968 | [Download](2500/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2500/previews/bikini.png) | [<NSFW, click to see>](2500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2500/previews/nude.png) | [<NSFW, click to see>](2500/previews/nude2.png) |  |  |
| 2000 | 0.964 | [Download](2000/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2000/previews/bikini.png) | [<NSFW, click to see>](2000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2000/previews/nude.png) | [<NSFW, click to see>](2000/previews/nude2.png) |  |  |
| 1500 | 0.953 | [Download](1500/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1500/previews/bikini.png) | [<NSFW, click to see>](1500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1500/previews/nude.png) | [<NSFW, click to see>](1500/previews/nude2.png) |  |  |
| 1000 | 0.801 | [Download](1000/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1000/previews/bikini.png) | [<NSFW, click to see>](1000/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1000/previews/nude.png) | [<NSFW, click to see>](1000/previews/nude2.png) |  |  |
| 500 | 0.581 | [Download](500/koshimizu_sachiko_idolmastercinderellagirls.zip) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](500/previews/bikini.png) | [<NSFW, click to see>](500/previews/bondage.png) |  |  |  | [<NSFW, click to see>](500/previews/nude.png) | [<NSFW, click to see>](500/previews/nude2.png) |  |  |
|
Bomml/Llama-2-70B-chat-w4-g128-awq | Bomml | 2023-09-16T03:09:15Z | 0 | 0 | null | [
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"awq",
"text-generation",
"en",
"arxiv:2307.09288",
"license:other",
"region:us"
]
| text-generation | 2023-09-16T02:50:36Z | ---
inference: false
language:
- en
license: other
model_creator: Meta Llama 2
model_link: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
model_name: Llama 2 70B Chat
model_type: llama
pipeline_tag: text-generation
quantized_by: Bomml.ai
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
- awq
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/lnPZ6Nr.png" alt="Bomml.ai" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.6em; margin-bottom: 0em;"><a href="https://bomml.ai">Support: Bomml.ai</a></p>
</div>
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Llama 2 70B Chat - W4 G128 AWQ
- Model creator: [Meta Llama 2](https://huggingface.co/meta-llama)
- Original model: [Llama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)
<!-- description start -->
## Description
This repo contains AWQ 4bit model files for [Meta Llama 2's Llama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf).
<!-- prompt-template start -->
## Prompt template: Llama-2-Chat
```
[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>
{prompt}[/INST]
```
<!-- prompt-template end -->
# Original model card: Meta Llama 2's Llama 2 70B Chat
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
|
TinyLlama/TinyLlama-1.1B-step-50K-105b | TinyLlama | 2023-09-16T03:06:11Z | 14,406 | 132 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-09-01T08:59:02Z | ---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
language:
- en
---
<div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
<div align="center">
<img src="./TinyLlama_logo.png" width="300"/>
</div>
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Model
This is an intermediate checkpoint with 50K steps and 105B tokens.
#### Releases Schedule
We will be rolling out intermediate checkpoints following the below schedule. We also include some baseline models for comparison.
| Date | HF Checkpoint | Tokens | Step | HellaSwag Acc_norm |
|------------|-------------------------------------------------|--------|------|---------------------|
| Baseline | [StableLM-Alpha-3B](https://huggingface.co/stabilityai/stablelm-base-alpha-3b)| 800B | -- | 38.31 |
| Baseline | [Pythia-1B-intermediate-step-50k-105b](https://huggingface.co/EleutherAI/pythia-1b/tree/step50000) | 105B | 50k | 42.04 |
| Baseline | [Pythia-1B](https://huggingface.co/EleutherAI/pythia-1b) | 300B | 143k | 47.16 |
| 2023-09-04 | [TinyLlama-1.1B-intermediate-step-50k-105b](https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b) | 105B | 50k | 43.50 |
| 2023-09-16 | -- | 500B | -- | -- |
| 2023-10-01 | -- | 1T | -- | -- |
| 2023-10-16 | -- | 1.5T | -- | -- |
| 2023-10-31 | -- | 2T | -- | -- |
| 2023-11-15 | -- | 2.5T | -- | -- |
| 2023-12-01 | -- | 3T | -- | -- |
#### How to use
You will need the transformers>=4.31
Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information.
```
from transformers import AutoTokenizer
import transformers
import torch
model = "PY007/TinyLlama-1.1B-step-50K-105b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
sequences = pipeline(
'The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.',
do_sample=True,
top_k=10,
num_return_sequences=1,
repetition_penalty=1.5,
eos_token_id=tokenizer.eos_token_id,
max_length=500,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
``` |
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